1

Deep Local Binary Patterns

arXiv:1711.06597v1 [] 17 Nov 2017

Kelwin Fernandes and Jaime S. Cardoso

Abstract—Local Binary Pattern (LBP) is a traditional descriptor for texture analysis that gained attention in the last
decade. Being robust to several properties such as invariance
to illumination translation and scaling, LBPs achieved stateof-the-art results in several applications. However, LBPs are
not able to capture high-level features from the image, merely
encoding features with low abstraction levels. In this work,
we propose Deep LBP, which borrow ideas from the deep
learning community to improve LBP expressiveness. By using
parametrized data-driven LBP, we enable successive applications
of the LBP operators with increasing abstraction levels. We
validate the relevance of the proposed idea in several datasets
from a wide range of applications. Deep LBP improved the
performance of traditional and multiscale LBP in all cases.

xref

xref

xref

(a) r=1, n=8

(b) r=2, n=16

(c) r=2, n=8

Figure 1: LBP neighborhoods with radius (r) and angular
resolution (n). The first two cases use Euclidean distance to
define the neighborhood, the last case use Manhattan distance.

Index Terms—Local Binary Patterns, Deep Learning, Image
Processing.
•

I. I NTRODUCTION

I

N recent years, computer vision community has moved towards the usage of deep learning strategies to solve a wide
variety of traditional problems, from image enhancement [1]
to scene recognition [2]. Deep learning concepts emerged from
traditional shallow concepts from the early years of computer
vision (e.g. filters, convolution, pooling, thresholding, etc.).
Although these techniques have achieved state-of-the-art
performance in several of tasks, the deep learning hype has
overshadowed research on other fundamental ideas. Narrowing
the spectrum of methods to a single class will eventually
saturate, creating a monotonous environment where the same
basic idea is being replicated over and over, and missing the
opportunity to develop other paradigms with the potential to
lead to complementary solutions.
As deep strategies have benefited from traditional -shallowmethods in the past, some classical methods started to take
advantage of key deep learning concepts. That is the case of
deep Kernels [3], which explores the successive application of
nonlinear mappings within the kernel umbrella. In this work,
we incorporate deep concepts into Local Binary Patterns [4],
[5], a traditional descriptor for texture analysis. Local Binary
Patterns (LBP) is a robust descriptor that briefly summarizes
texture information, being invariant to illumination translation
and scaling. LBP has been successfully used in a wide
variety of applications, including texture classification [6]–[9],
face/gender recognition [10]–[15], among others [16]–[18].
LBP has two main ingredients:
• The neighborhood (N ), usually defined by an angular
resolution (typically 8 sampling angles) and radius r
of the neighborhoods. Fig. 1 illustrates several possible
neighborhoods.
Kelwin Fernandes (kafc@inesctec.pt) and Jaime S. Cardoso
(jaime.s.cardoso@inesctec.pt) are with Faculdade de Engenharia da
Universidade do Porto and with INESC TEC, Porto, Portugal.

The binarization function b(xref , xi ) ∈ {0, 1}, which allows the comparison between the reference point (central
pixel) and each one of the points xi in the neighborhood.
Classical LBP is applicable when xref (and xi ) are in an
ordered set (e.g., R and Z), with b(xref , xi ) defined as
b(xref , xi ) = (xref ≺ xi ),

(1)

where ≺ is the order relation on the set (interpolation is
used to compute xi when a neighbor location does not
coincide with the center of a pixel).
The output of the LBP at each position ref is the code
resulting from the comparison (binarization function) of the
value xref with each of the xi in the neighborhood, with i ∈
N (ref ), see Figure 2. The LBP codes can be represented by
their numerical value as formally defined in (2).
X
LBP (xref ) =
2i · b(xref , xi )
(2)
i∈|N (ref )|

LBP codes can take 2|N | different values. In predictive
tasks, for typical choices of angular resolution, LBP codes are
compactly summarized into a histogram with 2|N | bins, being
this the feature vector representing the object/region/image
(see Fig. 3). Also, it is typical to compute the histograms
in sub-regions and to build a predictive model by using as
features the concatenation of the region histograms, being nonoverlapping and overlapping [19] blocks traditional choices
(see Figure 4).
In the last decade, several variations of the LBP have been
proposed to attain different properties. The two best-known
variations were proposed by Ojala et. al, the original authors
of the LBP methodology: rotation invariant and uniform LBP
[5].
Rotation invariance can be achieved by assigning a unique
identifier to all the patterns that can be obtained by applying
circular shifts. The new encoding is defined in (3), where

2

0

22
110
<
20

>

1

1

<
>

42
<

80

80

>

⇒

1

0

0

⇒ 10011010

>
<

0

0

1

60

1

(a) LBP code with 8 neighbors.
94

1

<
30

0

222
>

⇒

65
>
15

1

<

⇒

100001

>
>

40

0

0
0

22

(b) LBP code with 6 neighbors.

Figure 2: Cylinder and linear representation of the codes
at some pixel positions. Encodings are built in a clockwise
manner from the starting point indicated in the middle section
of both figures.
LBP encodings

⇒

Histogram

e1

e5

...

e7

e5

e8

e1

...

e7

e3

e10

e1

...

e6

..
.

..
.

..
.

..

..
.

e2

...

e2

e9

e10

.

⇒

Frequency

Original image

⇒

Classifier

e1 e2 e3 . . . en

e10

Encodings

Frequency

Figure 3: Traditional pipeline for image classification using
LBP.

e1 e2 e3 . . . en

Frequency

Encodings

e1 e2 e3 . . . en

Classifier

Frequency

Encodings

e1 e2 e3 . . . en

Frequency

Encodings

e1 e2 e3 . . . en
Encodings

Figure 4: Multi-block LBP with 2 × 2 non-overlapping blocks.

ROR(e, i) applies a circular-wrapped bit-wise shift of i positions to the encoding e.
LBP (xref ) = min{ROR(LBP (xref ), i) | i ∈ [0, . . . |N |)}
(3)

In the same work, Ojala et. al [5] identified that uniform
patterns (i.e. patterns with two or less circular transitions) are
responsible for the vast majority of the histogram frequencies,
leaving low discriminative power to the remaining ones. Then,
the relatively small proportion of non-uniform patterns limits
the reliability of their probabilities, all the non-uniform patterns are assigned to a single bin in the histogram construction,
while uniform patterns are assigned to individual bins. [5].
Heikkila et. al proposed Center-Symmetric LBP [20], which
increases robustness on flat image areas and improves computational efficiency. This is achieved by comparing pairs of
neighbors located in centered symmetric directions instead of
comparing each neighbor with the reference pixel. Thus, an
encoding with four bits is generated from a neighborhood
of 8 pixels. Also, the binarization function incorporates an
activation tolerance given by b(xi , xj ) = xi > xj + T . Further
extensions of this idea can be found in [21]–[23].
Local ternary patterns (LTP) are an extension of LBP that
use a 3-valued mapping instead of a binarization function.
The function used by LTP is formalized at (4). LTP are less
sensitive to noise in uniform regions at the cost of losing
invariance to illumination scaling. Moreover, LTP induce an
additional complexity in the number of encodings, producing
histograms with up to 3N bins.


−1
bT (xref , xi ) = 0


+1

xi < xref − T
xref − T ≤ xi ≤ xref + T
xref + T < xi

(4)

So far, we have mainly described methods that rely on
redefining the construction of the LBP encodings. A different
line of research focuses on improving LBP by modifying
the texture summarization when building the frequency histograms. Two examples of this idea were presented in this
work: uniform LBP [5] and multi-block LBP (see Fig. 4).
Since different scales may bring complementary information, one can concatenate the histograms of LBP values
at different scales. Berkan et al. proposed this idea in the
Over-Complete LBP (OCLBP) [19]. Besides computing the
encoding at multiple scales, OCLBP computes the histograms
on several spatially overlapped blocks. An alternative to this
way of modeling multiscale patterns is to, at each point,
compute the LPB code at different scales, concatenate the
codes and summarize (i.e., compute the histogram) of the
concatenated feature vector. This latter option has difficulties
concerning the dimensionality of the data (potentially tackled
with a bag of words approach) and the nature of the codes
(making unsuitable standard k-means to find the bins-centers
for a bag of words approach).
Multi-channel data (e.g. RGB) has been handled in a similar
way, by 1) computing and summarizing the LBP codes in each
channel independently and then concatenating the histograms
[24] and by 2) computing a joint code for the three channels
[25].
As LBP have been successfully used to describe spatial
relations between pixels, some works explored embedding
temporal information on LBP for object detection and background removal [10], [26]–[30].

It is trivial to prove that the only options for the binarization
function that hold Eq. (5) are the constant functions (always
zeros or always ones), the one given by Eq. (1) and its
reciprocal.

Model

Histogram

...

LBP

LBP

LBP

Image

3

Figure 5: Recursive application of Local Binary Patterns.

Finally, Local Binary Pattern Network (LBPNet) was introduced by Xi et al. [31] as a preliminary attempt to embed
Deep Learning concepts in LBP. Their proposal consists
on using a pyramidal approach on the neighborhood scales
and histogram sampling. Then, Principal Component Analysis
(PCA) is used on the frequency histograms to reduce the
dimensionality of the feature space. Xi et al. analogise the
pyramidal construction of LBP neighborhoods and histogram
sampling as a convolutional layer, where multiple filters operate at different resolutions, and the dimensionality reduction
as a Pooling layer. However, LBPNets aren’t capable of
aggregating information from a single resolution into higher
levels of abstraction which is the main advantage of deep
neural networks.
In the next sections, we will bring some ideas from the
Deep Learning community to traditional LBP. In this sense,
we intend to build LBP blocks that can be applied recursively
in order to build features with higher-level of abstraction.
II. D EEP L OCAL B INARY PATTERNS
The ability to build features with increasing abstraction level
using a recursive combination of relatively simple processing
modules is one of the reasons that made Convolutional Neural
Networks – and in general Neural Networks – so successful. In
this work, we propose to represent “higher order” information
about texture by applying LBP recursively, i.e., cascading LBP
computing blocks one after the other (see Fig 5). In this sense,
while an LBP encoding describes the local texture, a second
order LBP encoding describes the texture of textures.
However, while it is trivial to recursively apply convolutions
– and many other filters – in a cascade fashion, traditional LBP
are not able to digest their own output.
Traditional LBP rely on receiving as input an image with
domain in an ordered set (e.g. grayscale intensities). However,
LPB codes are not in an ordered set, dismissing the direct
recursive application of standard LBP. As such we will first
generalize the main operations supporting LBP and discuss
next how to assemble together a deep/recursive LBP feature
extractor. We start the discussion with the binarization function
b(xref , xi ).
It is instructive to think, in the conventional LBP, if nontrivial alternative functions exist to the adopted one, Eq. (1).
What is(are) the main property(ies) required by the binarization function? Does it need to make use of a (potentially implicit) order relationship? A main property of the binarization
function is to be independent of scaling and translation of the
input data, that is,
b(k1 xref + k2 , k1 xi + k2 ) = b(xref , xi ), k1 > 0.

(5)

Proof. Assume xi , xj > xref and b(xref , xi ) 6= b(xref , xj ).
Under the independence to translation and scaling (Eq. (5)),
b(xref , xi ) = b(xref , xj ) as shown below, which is a contradiction.
b(xref , xi )
= h Identity of multiplication i


xj − xref
xj − xref
b
xref ,
xi
xj − xref
xj − xref
= h Identity of addition i



xj − xref
xref xj − xref xj
xj − xref + (xi − xi )
xref ,
xi +
b
xj − xref
xj − xref
xj − xref
= hArithmetici


xi − xref
xj − xi xi − xref
xj − xi
b
xref + xref
,
xj + xref
x − xref
xj − xref xj − xref
x − xref
 j
 j
xi − xref
xj − xi
= Eq. (5), where k1 =
, k2 = xref
xj − xref
xj − xref
b(xref , xj )

Therefore, b(xref , xi ) must be equal to all xi above xref .
Similarly, b(xref , xi ) must be equal to all xi below xref .
Among our options, the constant binarization function is
not a viable option, since the information (in information
theory perspective) in the output is zero. Since the recursive
application of functions can be understood as a composition,
invariance to scaling and translation is trivially ensured by
using a traditional LBP in the first transformation.
Given that transitivity is a relevant property held by the
natural ordering of real numbers, we argue that such property
should be guaranteed by our binarization function. In this
sense, we will focus on strict partial orders of encodings.
Following, we show how to build such binarization functions
for the i-th application of the LBP operator, where i > 1. We
will consider both predefined/expert driven solutions and data
driven solutions (and therefore, application specific).
Hereafter, we will refer to the binarization function as the
partial ordering of LBP encodings. Despite the existence of
other types of functions may be of general interest, narrowing
the search space to those that can be represented as a partial
ordering induce efficient learning mechanisms.

A. Preliminaries
Let us formalize the deep binarization function as the order
relation b+ ∈ P(EN ×EN ), where EN is the set of encodings
induced by the neighborhood N .
Let Φ be an oracle Φ :: P(EN ×EN ) → R that assesses the
performance of a binarization function. For example, among
other options, the oracle can be defined as the performance
of the traditional LBP pipeline (see Fig. 3) on a dataset for a
given predictive task.

4

Table I: Lower bound of the number of combinations for
deciding the best LBP binarization function as partial orders.
# Neighbors
2
3
4
5
6
7
8

Rotational Inv.
2 · 101
5 · 102
3 · 106
7 · 1011
1 · 1036
2 · 1072
1 · 10225

Uniform
2 · 104
1 · 1015
2 · 1041
6 · 1094
2 · 10190
3 · 10346
1 · 10585

Traditional
5 · 102
7 · 1011
8 · 1046
2 · 10179
1 · 10685
3 · 102640
3 · 1010288

2) Learning b+ from a High-dimensional Space: A second
option is to map the code space to a new (higher-dimensional)
space that characterizes LBP encodings. Then, an ordering or
preference relationship can be learned in the extended space,
for instance resorting to preference learning algorithms [36]–
[38].
Some examples of properties that characterize LBP encodings are:
•
•

B. Deep Binarization Function

•

From the entire space of binarization functions, we restrict
our analysis to those induced by strict partial orders. Within
this context, it is easy to see that learning the best binarization
function by exhaustive exploration is intractable since the
number of combinations equals the number of directed acyclic
graph (DAG) with 2|N | = |EN | nodes. The DAG counting
problem was studied by Robinson [32] and is given by the
recurrence relation Eqs. (6)-(7).

•

a0 = 1
 
n
X
k−1 n
an>1 =
(−1)
2k(n−k) an−k
k

(6)
(7)

k=1

Table I illustrates the size of the search space for several
numbers of neighbors. For instance, for the traditional setting
with 8 neighbors, the number of combinations has more than
10,000 digits. Thereby, a heuristic approximation must be
carried out.
1) Learning b+ from a User-defined dissimilarity function:
The definition of a dissimilarity function between two codes
seems reasonably accessible. For instance, an immediate option is to adopt the hamming distance between codes, dH .
With rotation invariance in mind, one can adopt the minimum
hamming distance between all circularly shifted versions of
xref and xi , dri
H . The circular invariant hamming distance
between xref and xi can be computed as
dri
H =

min

s∈0,··· ,N −1

dH (ROR(xref , s), xi )

(8)

Having defined such a dissimilarity function between pairs
of codes, one can know proceed with the definition of the
binarization function.
Given the dissimilarity function, we can learn a mapping
of the codes to an ordered set. Resorting to Spectral Embedding [33], one can obtain such a mapping. The conventional
binarization function, Eq. (1), can then be applied. Other
alternatives for building the mappings can be found in the
manifold learning literature: Isomaps [34], Multidimensional
scaling (MDS) [35], among others. In this case, the oracle
function can be defined as the intrinsic loss functions used in
the optimization process of such algorithms.
Preserving a desired property P such as rotational invariance and sign invariance (i.e. interchangeability between
ones and zeros) can be achieved by considering P -aware
dissimilarities.

•

Number of transitions of size 1 (e.g. 101, 010).
Number of groups/transitions.
Size of the smallest/largest group.
Diversity on the group sizes.
Number of ones.

Techniques to learn the final ordering based on the new
high-dimensional space include:
•
•

Dimensionality reduction techniques, including Spectral
embeddings, PCA, MDS and other manifold techniques.
Preference learning strategies for learning rankings [36]–
[38].

A case of special interest that will be used in the experimental section of this work are Lexicographic Rankers (LR)
[36], [38]. In this work, we will focus on the simplest type
of LR, linear LR. Let us assume that features in the new
high-dimensional space are scoring rankers (e.g. monotonic
functions) on the texture complexity of the codes. Thus, for
each code ei and feature sj , the complexity associated to ei by
sj is denoted as sj (ei ). We assume sj (ei ) to lie in a discrete
domain with a well-known order relation.
Thus, each feature is grouping the codes into equivalence
classes. For example, the codes with 0 transitions (i.e. flat
textures), 2 transitions (i.e. uniform textures) and so on.
If we concatenate the output of the scoring rankers in a linear manner (s0 (ei ), s1 (ei ), · · · , sn (ei )), a natural arrangement
is their lexicographic order (see Eq. (9)), where each sj (ei )
is subordering the equivalence class obtained by the previous
prefix of rankers (s0 (ei ), · · · , sj−1 (ei )).


a=b



a ≺ b
LexRank(a, b) =

ab



LexRank(t(a), t(b))

, |a| = 0 ∨ |b| = 0
, a0 ≺ b0
, a0  b0
, a0 = b0
(9)
where t(a) returns the tail of the sequence. Namely, the order
between two encodings is decided by the first scoring ranker
in the hierarchy that assigns different values to the encodings.
Therefore, the learning process is reduced to find the best
feature arrangement. A heuristic approximation to this problem
can be achieved by iteratively appending to the sequence of
features the one that maximizes the performance of the oracle
Φ.
Similarly to property-aware dissimilarity functions, if the
features in the new feature vector V(x) are invariant to
P , the P -invariance of the learned binarization function is
automatically guaranteed.

5

Image

LBP

LBP-b+
1

···

LBP-b+
n
Histogram
Model

Figure 6: Deep LBP.

(a) Input image

(b) LBP

(c) DeepLBP(1)

(d) DeepLBP(2)

(e) DeepLBP(3)

(f) DeepLBP(4)

III. D EEP A RCHITECTURES
Given the closed form of the LBP with deep binarization
functions, their recursive combination seems feasible. In this
section, several alternatives for the aggregation of deep LBP
operators are proposed.
A. Deep LBP (DLBP)
The simplest way of aggregating Deep LBP operators
is by applying them recursively and computing the final
encoding histograms. Figure 6 shows this architecture. The
first transformation is done by a traditional shallow LBP
while the remaining transformations are performed using deep
binarization functions.
Figure 7 illustrates the patterns detected by several deep
levels on a cracker image from the Brodatz database. In this
case, the ordering between LBP encodings was learned by
using a lexicographic ordering of encodings on the number
of groups, the size of the largest group and imbalance ratio
between 0’s and 1’s. We can observe that the initial layers of
the architecture extract information from local textures while
the later layers have higher levels of abstraction.
B. Multi-Deep LBP (MDLBP)
Despite it may be a good idea to extract higher-order
information from images, for the usual applications of LBP,
it is important to be able to detect features at different levels
of abstraction. For instance, if the image has textures with
several complexity levels, it may be relevant to keep the
patterns extracted at different abstraction levels. Resorting to
the techniques employed in the analysis of multimodal data
[40], we can aggregate this information in two ways: feature
and decision-level fusion.
1) Feature-level fusion: one histogram is computed at each
layer and the model is built using the concatenation of all the
histograms as features.
2) Decision-level fusion: one histogram and decision model
is computed at each layer. The final model uses the probabilities estimated by each individual model to produce the final
decision.
Figures 8a and 8b show Multi-Deep LBP architectures with
feature-level and decision-level fusion respectively. In our
experimental setting, feature-level fusion was used.

Figure 7: Visualization of LBP encodings from a Brodatz
database [39] image. The results obtained by applying n
layers of Deep LBP operators are denoted as DeepLBP(n).
A neighborhood of size 8, radius 10 and Euclidean distance
was used. The grayscale intensity is defined by the order of
the equivalence classes.

Image

LBP

LBP-b+
1

Histogram

Histogram-1

...

LBP-b+
n
Histogram-n

..
.
Model

(a) Multi-Deep LBP with feature-level fusion.
Image

LBP

LBP-b+
1

Histogram

Histogram-1

...

LBP-b+
n
Histogram-n

..
.
Model-0

Model-1
Model

Model-n

Global Model

(b) Multi-Deep LBP with decision-level fusion.

Figure 8: Deep LBP architectures.

C. Multiscale Deep LBP (Multiscale DLBP)
In the last few years, deep learning approaches have benefited from multi-scale architectures that are able to aggregate

information from different image scales [41]–[43]. Despite
being able to induce higher abstraction levels, deep networks

6

Scale 1

Image

Deep LBP1

Scale 2
Image

Deep LBP2

..
.

...

Image

Deep LBPn

(a) KTH TIPS

(b) FMD

(c) Virus

(d) Brodatz

(e) Kylberg

(f) 102 Flowers

Model

Scale n

Figure 9: Multi-scale Deep LBP.
Table II: Summary of the datasets used in the experiments
Dataset
KTH TIPS
FMD
Virus
Brodatz*
Kylberg
102 Flowers
Caltech 101

Reference
[44]
[45]
[46]
[39]
[47]
[48]
[49]

Task
Texture
Texture
Texture
Texture
Texture
Object
Object

Images
810
1000
1500
1776
4480
8189
9144

Classes
10
10
15
111
28
102
102

are restrained to the size of the individual operators. Thereby,
aggregating multi-scale information in deep architectures may
exploit their capability to detect traits that appear at different
scales in the images in addition to turning the decision process
scale invariant.
In this work, we consider the stacking of independent deep
architectures at several scales. The final decision is done by
concatenating the individual information produced at each
scale factor (cf. Figure 9). Depending on the fusion approach,
the final model operates in different spaces (i.e. feature or
decision level). In an LBP context, we can define the scale
operator of an image by resizing the image or by increasing
the neighborhood radius.
IV. E XPERIMENTS
In this section, we compare the performance of the proposed
deep LBP architectures against shallow LBP versions. Several
datasets were chosen from the LBP literature covering a wide
range of applications, from texture categorization to object
recognition. Table II summarizes the datasets used in this
work. Also, Fig. 10 shows an image per dataset in order to
understand the task diversity.
We used a 10-fold stratified cross-validation strategy and the
average performance of each method was measured in terms
of:
•

Accuracy.

(g) Caltech 101

Figure 10: Sample images from each dataset

•

Class rank: Position (%) of the ground truth label in the
ranking of classes ordered by confidence. The ranking
was induced using probabilistic classifiers.

While high values are preferred when using accuracy, low
values are preferred for class rank. All the images were resized
to have a maximum size of 100 × 100 and the neighborhood
used for all the LBP operators was formed by 8 neighbors on
a radius of size 3, which proved to be a good configuration
for the baseline LBP. The final features were built using a
global histogram, without resorting to image blocks. Further
improvements in each application can be achieved by finetuning the LBP neighborhoods and by using other spatial
sampling techniques on the histogram construction. Since the
objective of this work was to objectively compare the performance of each strategy, we decided to fix these parameters.
The final decision model is a Random Forest with 1000 trees.
In the last two datasets, which contain more than 100 classes,
the maximum depth of the decision trees was bounded to 20
in order to limit the required memory.
In all our experiments, training data was augmented by
including vertical and horizontal flips.

7

Table III: Class rank (%) of the ground-truth label and accuracy with single-scale strategies
Dataset

KTH TIPS

FMD

Virus

Brodatz

Kylberg

102 Flowers

Caltech 101

Strategy
Similarity
High Dim
LBP
Similarity
High Dim
LBP
Similarity
High Dim
LBP
Similarity
High Dim
LBP
Similarity
High Dim
LBP
Similarity
High Dim
LBP
Similarity
High Dim
LBP
Similarity
High Dim

1
1.55
26.96
8.08
0.25
0.23
13.46
13.05
-

Class Rank
Layers
2
3
4
19.18
18.87 18.26
18.64
18.78
18.79
1.60
1.68
1.82
0.94
0.91
1.09
25.77
25.79
25.61
23.20
23.36
23.30
6.61
6.78
6.72
6.65
6.50
6.53
0.22
0.22
0.23
0.21
0.21
0.22
0.18
0.16
0.14
0.07
0.07
0.07
13.34
13.56
13.99
13.10 12.99
13.15
12.37
12.23
12.32
11.98
12.19
12.16

A. Single-scale
First, we validated the performance of the proposed deep
architectures on single scale settings with increasing number
of deep layers. Information from each layer was merged at
a feature level by concatenating the layerwise histogram (c.f.
Section III-B). Table III summarizes the results of this setting.
In all the datasets, the proposed models surpassed the results
achieved by traditional LBP.
Furthermore, even when the accuracy gains are small, the
large gains in terms of class rank suggest that the deep
architectures induce more stable models, which assign a high
probability on the ground-truth level, even on misclassified
cases. For instance, in the Kylberg dataset, a small relative
accuracy gain of 3.23% was achieved by the High Dimensional
rule, the relative gain on the class rank was 69.56%.
With a few exceptions (e.g. JAFFE dataset), the data-driven
deep operator based on a high dimensional projection achieved
the best performance. Despite the possibility to induce encoding orderings using user-defined similarity functions, the final
orderings are static and domain independent. In this sense,
more flexible data-driven approaches as the one suggested in
Section II-B2 are able to take advantage of the dataset-specific
properties.
Despite the capability of the proposed deep architectures to
achieve large gain margins, the deep LBP operators saturate
rapidly. For instance, most of the best results were found on
architectures with up to three deep layers. Further research on
aggregation techniques to achieve higher levels of abstraction
should be conducted. For instance, it would be interesting
to explore efficient non-parametric approaches for building
encoding orderings that allow more flexible data-driven optimization.

5
18.80
19.10
1.86
1.11
25.59
23.50
6.73
6.55
0.25
0.23
0.14
0.07
14.29
13.32
12.38
12.34

1
89.22
29.20
56.80
89.23
95.29
23.18
39.71
-

2
53.28
53.28
88.96
92.96
28.90
33.40
61.00
61.53
89.73
90.72
96.14
98.37
25.59
24.56
40.35
41.45

Accuracy
Layers
3
53.28
53.28
88.57
93.58
30.00
32.60
61.33
61.27
90.23
90.72
96.52
98.35
24.46
23.76
40.07
40.78

4
53.28
53.28
86.99
92.72
30.90
33.30
60.93
61.47
90.50
90.09
96.72
98.26
24.92
22.81
39.74
40.56

5
53.28
53.28
87.97
92.36
30.80
33.00
61.93
62.27
90.36
89.59
96.81
98.24
24.58
22.36
39.81
40.43

B. Multi-Scale
A relevant question in this context is if the observed gains
are due to the higher abstraction levels of the deep LBP
encodings or to the aggregation of information from larger
neighborhoods. Namely, when applying a second order operator, the neighbors of the reference pixel include information
from their own neighborhood which was initially out of the
scope of the local LBP operator. Thereby, we compare the
performance of the Deep LBP and multiscale LBP.
In order to simplify the model assessment, we fixed the
number of layers to 3 in the deep architectures. A scaling factor of 0.5 was used on each layer of the pyramidal multiscale
operator. Guided by the results achieved in the single-scale
experiments, the deep operator based on the lexicographic
sorting of the high-dimensional feature space was used in all
cases.
Table IV summarizes the results on the multiscale settings.
In most cases, all the deep LBP architectures surpassed
the performance of the best multiscale shallow architecture.
Thereby, the aggregation level achieved by deep LBP operators
goes beyond a multiscale analysis, being able to address
meta-texture information. Furthermore, when combined with
a multiscale approach, deep LBP achieved the best results in
all the cases.
C. LBPNet
Finally, we compare the performance of our deep LBP
architecture against the state of the art LBPNet [31]. As
referred in the introduction, LBPNet uses LBP encodings at
different neighborhood radius and histogram sampling in order
to simulate the process of learning a bag of convolutional
filters in deep networks. Then, the dimensionality of the
descriptors are reduced by means of PCA, resorting to the
idea of pooling layers from Convolutional Neural Networks

8

Table IV: Class rank (%) of the ground-truth label and
accuracy with multi-scale strategies
Dataset
KTH TIPS
FMD
Virus
Brodatz
Kylberg
102 Flowers
Caltech 101

Strategy
Shallow
Deep
Shallow
Deep
Shallow
Deep
Shallow
Deep
Shallow
Deep
Shallow
Deep
Shallow
Deep

Class Rank
Scales
1
2
3
1.55
1.17
1.22
0.91
0.79
0.62
26.96 26.31 26.32
23.36 23.54 23.77
8.08
7.51
7.97
6.50
5.92
6.04
0.25
0.20
0.23
0.21
0.13
0.13
0.23
0.13
0.12
0.07
0.05
0.04
13.46 13.10 12.79
12.99 12.68 12.71
12.92 12.46 12.28
12.21 11.74 11.60

Accuracy
Scales
1
2
3
89.22 90.94 90.93
93.58 94.21 94.96
29.20 29.60 29.80
32.60 33.20 33.00
56.80 60.60 58.60
61.27 66.13 64.87
89.23 90.77 90.00
90.72 92.97 93.11
95.29 97.34 97.57
98.35 98.84 98.95
23.18 25.10 26.40
23.76 26.02 26.87
40.07 40.84 41.03
40.68 41.67 42.01

Table V: Class rank (%) of the ground-truth label and accuracy
Dataset
KTH TIPS

FMD

Virus

Brodatz

Kylberg

(CNN). However, the output of a LBPNet cannot be used by
itself in successive calls of the same function. Thereby, it is
uncapable of building features with higher expresiveness than
the individual operators.
In our experiments, we considered the best LBPNet with
up to three scales and histogram computations with nonoverlapping histograms that divide the image in 1 × 1, 2 × 2
and 3 × 3 blocks. The number of components kept in the
PCA transformation was chosen in order to retain 95% of the
variance for most datasets with the exception of 102 Flowers
and Caltech, where a value of 99% was chosen due to poor
performance of the previous value. A global histogram was
used in our deep LBP architecture
Table V summarizes the results obtained by multiscale LBP
(shallow), LBPNet and our proposed deep LBP. In order to
understand if the gains achieved by the LBPNet are due
to the overcomplete sampling or to the PCA transformation
preceeding the final classifier, we validated the performance
of our deep architecture with a PCA transformation on the
global descriptor before applying the Random Forest classifier.
Despite being able to surpass the performance of our deep LBP
without dimensionality reduction, LBPNet did not improve
the results obtained by our deep architecture with PCA in
most cases. In this sense, even whithout resorting to local
descriptors on the histogram sampling, our model was able
to achieve the best results within the family of LBP methods.
The only exception was observed in the 102 Flowers dataset
(see Fig. 10f), where the spatial information can be relevant.
It is important to note that our model can also benefit from
using spatial sampling of the LBP activations. Moreover,
deep learning concepts such as dropout and pooling layers
can be introduced within the Deep LBP architectures in a
straightforward manner.
V. C ONCLUSIONS
Local Binary Patterns have achieved competitive performance in several computer vision tasks, being a robust and
easy to compute descriptor with high discriminative power
on a wide spectrum of tasks. In this work, we proposed Deep
Local Binary Patterns, an extension of the traditional LBP that

102 Flowers

Caltech 101

Strategy
Shallow
LBPNet
Deep LBP
Deep LBP (PCA)
Shallow
LBPNet
Deep LBP
Deep LBP (PCA)
Shallow
LBPNet
Deep LBP
Deep LBP (PCA)
Shallow
LBPNet
Deep LBP
Deep LBP (PCA)
Shallow
LBPNet
Deep LBP
Deep LBP (PCA)
Shallow
LBPNet
Deep LBP
Deep LBP (PCA)
Shallow
LBPNet
Deep LBP
Deep LBP (PCA)

Class Rank
1.17
0.43
0.62
0.16
26.31
25.71
23.36
23.20
7.51
7.18
5.92
5.91
0.20
0.20
0.13
0.12
0.12
0.19
0.04
0.02
12.79
9.61
12.68
22.30
12.46
12.11
11.60
10.87

Accuracy
90.94
96.29
94.96
98.39
29.80
30.00
33.20
32.30
60.60
60.73
66.13
65.60
90.77
91.49
93.11
94.46
97.57
95.80
98.95
99.55
26.40
35.56
26.87
8.80
41.03
42.69
42.01
45.14

allow successive applications of the operator. By applying LBP
in a recursive way, features with higher level of abstraction are
computed that improve the descriptor discriminability.
The key aspect of our proposal is the introduction of
flexible binarization rules that define an order relation between
LBP encodings. This was achieved with two main learning
paradigms. First, learning the ordering based on a userdefined encoding similarity metric. Second, allowing the user
to describe LBP encodings on a high-dimensional space and
learning the ordering on the extended space directly. Both
ideas improved the performance of traditional LBP in a diverse
set of datasets, covering various applications such as face analysis, texture categorization and object detection. As expected,
the paradigm based on a projection to a high-dimensional
space achieved the best performance, given its capability of
using application specific knowledge in an efficient way. The
proposed deep LBP are able to aggregate information from
local neighborhoods into higher abstraction levels, being able
to surpass the performance obtained by multiscale LBP as
well.
While the advantages of the proposed approach were
demonstrated in the experimental section, further research
can be conducted on several areas. For instance, it would be
interesting to find the minimal properties of interest that should
be guaranteed by the binarization function. In this work, since
we are dealing with intensity-based image, we restricted our
analysis to partial orderings. However, under the presence of
other types of data such as directional (i.e. periodic, angular)
data, cycling or local orderings could be more suitable. In the
most extreme case, the binarization function may be arbitrarily
complex without being restricted to strict orders.
On the other hand, constraining the shape of the binarization

9

function allows more efficient ways to find suitable candidates.
In this sense, it is relevant to explore ways to improve the
performance of the similarity-based deep LBP. Two possible
options would be to refine the final embedding by using
training data and allowing the user to specify incomplete
similarity information.
In this work, each layer was learned in a local fashion,
without space for further refinement. While this idea was
commonly used in the deep learning community when training
stacked networks, later improvements take advantage of refining locally trained architectures [50]. Therefore, we plan to
explore global optimization techniques to refine the layerwise
binarization functions.
Deep learning imposed a new era in computer vision
and machine learning, achieving outstanding results on applications where previous state-of-the-art methods performed
poorly. While the foundations of deep learning rely on very
simple image processing operators, relevant properties held
by traditional methods, such as illumination and rotational
invariance, are not guaranteed. Moreover, the amount of data
required to learn competitive deep models from scratch is
usually prohibitive. Thereby, it is relevant to explore the path
into a unification of traditional and deep learning concepts.
In this work, we explored this idea within the context of
Local Binary Patterns. The extension of deep concepts to other
traditional methods is of great interest in order to rekindle the
most fundamental concepts of computer vision to the research
community.
ACKNOWLEDGMENT
This work was funded by the Project “NanoSTIMA: Macroto-Nano Human Sensing: Towards Integrated Multimodal
Health Monitoring and Analytics/- NORTE-01-0145-FEDER000016” financed by the North Portugal Regional Op-erational
Programme (NORTE 2020), under the PORTUGAL 2020
Partnership Agreement, and through the European Regional
Development Fund (ERDF), and also by Fundação para a
Ciência e a Tecnologia (FCT) within PhD grant number
SFRH/BD/93012/2013.
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