arXiv:1710.07198v1 [] 19 Oct 2017

Dress like a Star: Retrieving Fashion Products from Videos
Noa Garcia

George Vogiatzis

Aston University, UK

Aston University, UK

garciadn@aston.ac.uk

g.vogiatzis@aston.ac.uk

Abstract
This work proposes a system for retrieving clothing and
fashion products from video content. Although films and
television are the perfect showcase for fashion brands to
promote their products, spectators are not always aware of
where to buy the latest trends they see on screen. Here,
a framework for breaking the gap between fashion products shown on videos and users is presented. By relating
clothing items and video frames in an indexed database and
performing frame retrieval with temporal aggregation and
fast indexing techniques, we can find fashion products from
videos in a simple and non-intrusive way. Experiments in a
large-scale dataset conducted here show that, by using the
proposed framework, memory requirements can be reduced
by 42.5X with respect to linear search, whereas accuracy is
maintained at around 90%.

Figure 1: Fashion items in a popular TV show.
difficult and it involves time-consuming searches.
To help in the task of finding fashion products that appear in multimedia content, some websites, such as Film
Grab4 or Worn on TV 5 , provide catalogs of items that can
be seen on films and TV shows, respectively. These websites, although helpful, still require some effort before actually buying the fashion product: users need to actively
remember items from videos they have previously seen and
navigate through the platform until they find them. On the
contrary, we propose an effortless, non-intrusive and fast
computer vision tool for searching fashion items in videos.
This work proposes a system for retrieving clothing
products from large-scale collections of videos. By taking
a picture of the playback device during video playback (i.e.
an image of the cinema screen, laptop monitor, tablet, etc.),
the system identifies the corresponding frame of the video
sequence and returns that image augmented with the fashion
items in the scene. In this way, users can find a product as
soon as they see it by simple taking a photo of the video sequence where the product appears. Figure 1 shows a frame

1. Introduction
Films and TV shows are a powerful marketing tool for
the fashion industry, since they can reach thousands of millions of people all over the world and impact on fashion
trends. Spectators may find clothing appearing in movies
and television appealing and people’s personal style is often
influenced by the multimedia industry. Also, online videosharing websites, such as YouTube1 , have millions of users
generating billions of views every day2 and famous youtubers3 are often promoting the latest threads in their videos.
Fashion brands are interested in selling the products that
are advertised in movies, television or YouTube. However,
buying clothes from videos is not straightforward. Even
when a user is willing to buy a fancy dress or a trendy pair
of shoes that appear in the latest blockbuster movie, there
is often not enough information to complete the purchase.
Finding the item and where to buy it is, most of the times,
1 https://www.youtube.com/
2 https://www.youtube.com/yt/press/statistics.html

4 http://filmgarb.com/

3 Users

5 https://wornontv.net

who have gained popularity from their videos on YouTube

1

of a popular TV show along with its fashion items.
Recently, many computer vision applications for clothing retrieval [14, 11] and style recommendation [24, 21]
have been proposed. Our system is close to clothing retrieval approaches in that the aim is to retrieve a product
from an image. Instead of retrieving products directly as
in standard clothing retrieval, we propose to first retrieve
frames from the video collection. There are several reasons
for that. Firstly, in standard clothing retrieval users usually
provide representative images of the object of interest (e.g.
dresses in front view, high-heeled shoes in side view, etc.).
In a movie, the view of the object of interest cannot be chosen, and items might be partially or almost completely occluded, such as the red-boxed dress in Figure 1. Secondly,
standard clothing retrieval requires to select a bounding box
around the object of interest. This is undesirable in clothing
video retrieval as it may distract user’s attention from the
original video content. Finally, performing frame retrieval
instead of product retrieval in videos allows users to get
the complete list of fashion items in a scene, which usually
matches the character’s style, including small accessories
such as earrings, watches or belts. Some of these items are
usually very small, sometimes almost invisible, and would
be impossible to detect and recognize using standard object
retrieval techniques.
Retrieving frames from videos is a challenging task in
terms of scalability. An average movie of two hours duration may contain more than 200,000 frames. That means
that with only five movies in the video dataset, the number
of images in the collection might be over a million. To overcome scalability issues, we propose the combination of two
techniques. Firstly, frame redundancy is exploited by tracking and summarizing local binary features [7, 20] into a key
feature. Secondly, key features are indexed in a kd-tree for
fast search of frames. Once the scene the query image belongs to is identified, the clothing items associated to that
scene are returned to the user.
The contributions of this paper are:
• Introduction of the video clothing retrieval task and
collection of a dataset of videos for evaluation.
• Proposal of a fast and scalable video clothing retrieval
framework based on frame retrieval and indexing algorithms.
• Exhaustive evaluation of the framework, showing that
similar accuracy to linear search can be achieved while
the memory requirements are reduced by a factor of
42.5.
This paper is structured as follows: related work is summarized in Section 2; the details of the proposed system
are explained in Section 3; experiment setup and results are
detailed in Section 4 and 5, respectively; finally, the conclusions are summarized in Section 6.

2. Related Work
Clothing Retrieval. Clothing retrieval is the field concerned with finding similar fashion products given a visual
query. In the last few years, it has become a popular field
within the computer vision community. Proposed methods
[14, 25, 13, 11, 12, 15] are mainly focused on matching realworld images against online catalog photos. In this crossscenario problem, some methods [25, 13, 15] train a set of
classifiers to find items with similar attributes. Other approaches [14, 12, 11] model the differences across domains
by using a transfer learning matrix [14] or a deep learning
architecture [12, 11]. All these methods, however, perform
clothing retrieval on static images, in which a representative
and recognizable part of the item of interest is contained. In
the scenario of clothing retrieval from films and television,
fashion items are not always shown in a representative and
recognizable way across the whole duration of the video
due to object occlusions and changes in camera viewpoints.
Given a snapshot of a video, an alternative solution is to
first, identify the frame it belongs to and then, find the products associated to it.
Scene Retrieval. Scene retrieval consists on finding similar frames or scenes from a video collection according to a
query image. Early work, such as Video Google [22] and
others [18, 9, 8], retrieve frames from video datasets by applying image retrieval methods and processing each frame
independently. More recent scene retrieval approaches use
temporal aggregation methods to improve scalability and
reduce memory requirements. [2, 5] extract hand-crafted
local features from frames, track them along time and aggregate them into a single vector. Other proposals [26, 3, 4]
produce a single scene descriptor, which improves efficiency but involves a more difficult comparison against the
static query image. Recently, Araujo et al. [4] showed that,
in scene retrieval, methods based on hand-crafted features
outperform convolutional neural networks.
Similarly to [2], our approach is based on the aggregation
of local features along temporal tracks. Instead of using
SIFT features [16], we provide evidence that binary features
[7, 20] are more appropriate for the task and that they can
be easily indexed in a kd-tree for fast search.

3. Video Clothing Retrieval Framework
To retrieve fashion products from movies, television and
YouTube, we propose a framework based on frame retrieval
and fast indexing techniques. Since the number of frames
explodes with the number of movies available in a collection, a key feature of this framework is its scalability. The
system overview is shown in Figure 2. There are three main
modules: product indexing, training phase and query phase.
The product indexing and the training phase are done offline, whereas the query phase is performed online.

Product Indexing

Offline

Training phase

Fashion Items

Indexed Database

Video Collection

Feature
Extraction

Feature
Tracking

Shot
Detection

Key Feature
Computation

Key Feature
Indexing

Video Frames

Online

Output

Query phase

Retrieved Frame
Feature
Extraction

Key Feature
Search

Shot
Search

Fashion Items

Frame
Search

Query Image

Figure 2: System overview. First, during the product indexing, fashion items and frames are related in an indexed database.
Then, frames are indexed by using key features. Finally, in the query phase, query images are used to retrieve frames and find
associated fashion items.
In the product indexing, fashion items and frames from
the video collection are related in an indexed database. This
process can be done manually (e.g. with Amazon Mechanical Truck6 ) or semi-automatically with the support of a standard clothing retrieval algorithm. In the training phase, features are extracted and tracked along time. Tracks are used
to detect shot boundaries and compute aggregated key features, which are indexed in a kd-tree. In the query phase,
features extracted from the query image are used to find
their nearest key features in the kd-tree. With those key
features, the shot and the frame the query image belong to
are retrieved. Finally, the fashion products associated with
the retrieved frame are returned by the system.

3.1. Feature Extraction and Tracking
Local features are extracted from every frame in the
video collection and tracked across time by applying descriptor and spatial filters. The tracking is performed in a
bidirectional way so features within a track are unique (i.e.
each feature can only be matched with up to two features:
one in the previous frame and one in the following frame).
6 https://www.mturk.com

As local features, instead of the popular SIFT [16], we
use binary features for two main reasons. Firstly, because
Hamming distance for binary features is faster to compute
than Euclidean distance for floating-points vectors. Secondly, because we find that binary features are more stable
over time than SIFT, as can be seen in Figure 3. Convolutional neural network (CNN) features from an intermediate
layer of a pre-trained network were also studied. However,
their results were not satisfactory as the resulting tracked
features quickly started to get confused when the dataset
increased.

3.2. Shot Detection and Key Feature Computation
Consecutive frames that share visual similarities are
grouped into shots. The boundaries of different shots are
detected when two consecutive frames have no common
tracks. Each shot contains a set of tracks, each track representing the trajectory of a particular feature along time.
We define a key feature as the aggregation of all the features
in the same track into a single vector. Subsequently, each
shot is then represented by a set of key features, similarly
to how frames are represented by a set of features. For each
track, a key feature is computed by using majorities [10].

Figure 4: Visual similarities between frames. Left: Query
image. Middle: Ground truth frame. Right: Retrieved
frame similar to ground truth frame, thus, Visual Match.
FIFO queue we first explore the closest nodes to the root,
because the corresponding bits exhibit more variability by
construction of the tree.

3.4. Search

Figure 3: Trajectories of sample tracks along a sequence of
frames. Left: Binary features. Right: SIFT features. Binary
features are more constant over time than SIFT features.

3.3. Key Feature Indexing
A popular method for searching in binary feature space
is FLANN [17]. FLANN uses multiple, randomly generated hierarchical structures and it is not obvious how it can
be deployed across multiple CPUs in order to provide the
scalability we are looking for. On the other hand, kd-trees
are a formalism that has been shown to be highly parallelizable in [1]. In this work, we modify the basic kd-tree to
handle binary features and index our key features.
In a kd-tree each decision node has an associated dimension, a splitting value and two child nodes. If the value of a
query vector in the associated dimension is greater than the
splitting value, the vector is assigned to the left child. Otherwise, the vector is associated to the right child. The process
is repeated at each node during the query phase until a leaf
node is reached. In our case, as we are dealing with binary
features, each decision node has an associated dimension,
dim, such that all query vectors, v, with v[dim] = 1 belong
to the left child, and all vectors with v[dim] = 0 belong to
the right child. The value dim is chosen such that the training data is split more evenly in that node, i.e. its entropy
is maximum. Note that this criterion is similar to the one
used in the ID3 algorithm [19] for the creation of decision
trees, but where the splitting attribute is chosen as the one
with smallest entropy. Leaf nodes have as many as SL indices pointing to the features that ended up in that node. A
first-in first-out (FIFO) queue keeps record of the already
visited nodes to backtrack B times and explore them later.
We use a FIFO queue to ensure that even if some of the bits
in a query vector are wrong, the vector can reach its closest neighbours by exploring unvisited nodes latter. With the

In the query phase, binary features are extracted from an
input image and assigned to its nearest set of key features by
searching down the kd-tree. Each key feature votes for the
shot it belongs to. The set of frames contained in the most
voted shot are compared against the input image by brute
force, i.e. distances between descriptors in the query image
and descriptors in the candidate frames are computed. The
frame with minimum distance is retrieved. Shots are commonly groups of a few hundreds of frames, thus the computation can be performed very rapidly when applying the
Hamming distance. Finally, all the fashion vectors associated with the retrieved frame are returned to the user.

4. Experiments
Experimental Details. To evaluate our system a collection of 40 movies with more than 80 hours of video and up
to 7 million frames is used. Query images are captured by
a webcam (Logitech HD Pro Webcam C920) while movies
are being played on a laptop screen. To provide a ground
truth for our experiments, the frame number of each captured image is saved in a text file. Movies and queries have
different resolutions and aspect ratios, thus all the frames
and images are scaled down to 720 pixels in width. Binary features are computed by using ORB detector [20] and
BRIEF extractor [7]. In the tracking module, only matches
with a Hamming distance less than 20 and a spatial distance less than 100 pixels are considered, whereas in the
key feature computation algorithm, only tracks longer than
7 frames are used. The default values for the kd-tree are set
at SL = 100 and B = 50.
Evaluation criteria. The aim of the system is to find
the fashion products in the query scene, so we only retrieve
one frame per query image. The returned frame may not be
the same frame as the one in the annotated ground truth but
one visually similar. As long as the retrieved frame shares
strong similarities with the ground truth frame, we consider
it as a Visual Match, as shown in Figure 4. To measure the
visual similarities between two frames, we match SURF [6]

BF

KT

KF

Ours

85M

85M

25M

2M

2.53GB

2.53GB

762MB

61MB

0.90

0.91

0.92

0.94

0.92

0.93

B = 100

0.96

0.93

0.94

B = 250

0.97

0.93

0.94

Indexed
Features
Memory
B = 10

(b)

Figure 5: Precision of the evaluation method: (a) TPR and
FPR curves; (b) Three ground truth frames (left column)
and retrieved frames (right column) along with their score.
features. The linear comparison between two frames that
do not present any noise or perspective distortion is an easy
task that almost all kinds of features can perform correctly.
If the matching score between SURF features of two dataset
frames is greater than a threshold, τ , they are considered to
be visually similar.
To measure the precision of this evaluation method, we
manually annotate either if a pair of frames (i.e. ground
truth frame and retrieved frame) is a Visual Match or not,
along with its score. For different values of τ the True Positive Rate (TPR) as well as the False Positive Rate (FPR) are
computed as:
TPR =

TP
TP + FN

FPR =

FP
FP + TN

(1)

where T P is the number of true positives (Visual Match
with score > τ ), F P is the number of false positives (No
Match with score > τ ), T N is the number of true negatives
(No Match with score ≤ τ ) and F N is the number of false
negatives (Visual Match with score ≤ τ ). Figure 5 shows
both the TPR and the FPR computed with the annotations of
615 pairs of frames and 406 different values of τ . Among all
the possible values, we chose τ = 0.15, with T P R = 0.98
and F P R = 0. Finally, the accuracy for a set of query
images is computed as:
Acc =

No. Visual Matches
Total No. Queries

(2)

5. Experimental Results
To evaluate the proposed framework, two different experiments are performed. In the first one, our frame retrieval
system is compared against other retrieval systems. In the
second one, the performance of the frame retrieval method
when scaling up the video collection is evaluated in terms
of accuracy.

5.1. Retrieval Performance
First, we compare our system against other retrieval
frameworks.

Accuracy

(a)

B = 50

0.98

Table 1: Comparison between different systems on a single
movie. Accuracy is shown for four backtracking steps, B.
Our system achieves comparable results by using 42.5x less
memory than BF and KT and 12.5x less memory than KF.

BF. Brute Force matcher, in which query images are
matched against all frames and all features in the database.
Brute Force system is only used as an accuracy benchmark,
since each query take, in average, 46 minutes to be processed. Temporal information is not used.
KT. Kd-Tree search, in which all features from all
frames are indexed using a kd-tree structure, similarly to
[1]. Temporal information is not used.
KF. Key Frame extraction method [23], in which temporal information is used to reduce the amount of frames
of each shot into a smaller set of key frames. Key frames
are chosen as the ones at the peaks of the distance curve
between frames and a reference image computed for each
shot. For each key frame, features are extracted and indexed
in a kd-tree structure.
We compare these three systems along with the method
proposed here, using a single movie, The Devil Wears
Prada, consisting of 196,572 frames with a total duration
of 1 hour 49 minutes and 20 seconds. To be fair in our comparison, we use binary features in each method. The results
of this experiment are detailed in Table 1. The performance
is similar for the four systems. However, the amount of
processed data and the memory requirements are drastically
reduced when the temporal information of the video collection is used. In the case of our system, by exploiting temporal redundancy between frames, the memory is reduced
by 42.5 times with respect to BF and KT and by 12.5 times
with respect to the key frame extraction technique. Theoretically, that means that when implemented in a distributed
kd-tree system as the one in [1], where the authors were
able to process up to 100 million images, our system might
be able to deal with 4,250 million frames, i.e. more than

Title

N. Frames

N. Features

N. Shots

N. Key Features

N. Queries

Accuracy

The Help

210387

101M

1726

2.2M

813

0.98

Intolerable Cruelty

179234

86M

1306

2M

544

0.97

Casablanca

147483

71M

881

1.5M

565

0.96

Witching & Bitching

163069

66M

4193

0.8M

588

0.74

Pirates of the Caribbean 3

241127

108M

3695

1.7M

881

0.74

Captain Phillips

190496

59M

7578

0.6M

618

0.67

7M

3040M

116307

58M

25142

0.87

Total

Table 2: Summary of the results from the experiments on the dataset containing 40 movies. Due to space restrictions, only
three of the best and worst accuracies are shown, along with the total results for the whole database.
Pirates of the Caribbean 3 perform worst, as fewer descriptors can be found in those kinds of dimly lit images.
Figure 6 shows the evolution of accuracy when the size
of the database increases for five different movies. It can
be seen that most of the movies are not drastically affected
when the number of frames in the database is increased
from 200,000 to 7 million. For example, both Intolerable
Cruelty and 12 Years a Slave movies maintain almost a constant accuracy for different sizes of the collection. Even
in the worst case scenario, The Devil Wears Prada movie,
the loss in accuracy is less than a 8.5%. This suggests that
our frame retrieval system is enough robust to handle largescale video collections without an appreciable loss in performance.
Figure 6: Accuracy vs Database size for 5 different movies.

20,000 movies and 40,000 hours of video.

5.2. Large-scale Dataset
In this experiment, we explore the scalability of our
framework by increasing the dataset up to 40 movies and 7
million frames. The collection is diverse and it contains different movie genres such as animation, fantasy, adventure,
comedy or drama, to ensure that a wide range of movies can
be indexed and retrieved with the proposed algorithm.
Table 2 shows the results of this experiment. It can be
seen that, by using our temporal aggregation method, the
amount of data is reduced from 7 million frames and 3,040
million features to only 116,307 shots and 58 million key
features. Even so, the total number of key features in the 40
movie collection is still smaller than the 80 million features
that, in average, a single movie contains. The total accuracy
over the 40 movies is 0.87, reaching values of 0.98 and 0.97
in The Help and Intolerable Cruelty movies, respectively.
Movies with very dark scenes such as Captain Phillips and

6. Conclusions
This work proposes a framework to perform video clothing retrieval. That is, given a snapshot of a video, identify
the video frame and retrieve the fashion products that appear in that scene. This task would help users to easily find
and purchase items shown in movies, television or YouTube
videos. We propose a system based on frame retrieval and
fast indexing. Experiments show that, by using our temporal aggregation method, the amount of data to be processed
is reduced by a factor of 42.5 with respect to linear search
and accuracy is maintained at similar levels. The proposed
system scales well when the number of frames is increased
from 200,000 to 7 million. Similar to [1] our system can
easily be parallelised across multiple CPUs. Therefore, the
encouraging experimental results shown here indicate that
our method has the potential to index fashion products from
thousands of movies with high accuracy. In future work we
intend to further increase the size of our dataset in order
to identify the true limits of our approach. Furthermore it
is our intention to make all data publicly available so that
other researchers can investigate this interesting retrieval
problem.

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