84347
Latent Layout Analysis for Discovering Objects in Images
Latent layout analysis (LLA) is a novel unsupervised learning technique to discover objects in unseen images using a set of un-annotated training images. LLA defines a generative model that associates latent aspects to local appearances. The dependency between aspects and position is captured by a spatial sensitive aspect model. This dependency distinguishes LLA from probabilistic latent semantic analysis (PLSA). The latent aspects together with the latent layout constitute a compact scene representation. We demonstrate that the proposed LLA significantly outperforms probabilistic latent semantic analysis in two tasks: object discovery (detection) and object localization
12777678
Unsupervised Image Layout Extraction
We propose a novel unsupervised learning algorithm to extract the layout of an image by learning latent object-related aspects. Unlike traditional image segmentation algorithms that segment an image using feature similarity, our method is able to learn high-level object characteristics (aspects) from a large number of unlabeled images containing similar objects to facilitate image segmentation. Our method does not require human to annotate the training set and works without supervision. We use a graphical model to address the learning of aspects and layout extraction together. In particular, aspect-feature dependency from multiple images is learned via the expectation-maximization algorithm. We demonstrate that, by associating latent aspects to spatial structure, the proposed method achieves much better layout extraction results than using probabilistic latent semantic analysis.
1435851
Soft shape context for iterative closest point registration
This paper introduces a shape descriptor, the soft shape context, motivated by the shape context method. Unlike the original shape context method, where each image point was hard assigned into a single histogram bin, we instead allow each image point to contribute to multiple bins, hence more robust to distortions. The soft shape context can easily be integrated into the iterative closest point (ICP) method as an auxiliary feature vector, enriching the representation of an image point from spatial information only, to spatial and shape information. This yields a registration method more robust than the original ICP method. The method is general for 2D shapes. It does not calculate derivatives, hence being able to handle shapes with junctions and discontinuities. We present experimental results to demonstrate the robustness compared with the standard ICP method.
15233730
Circulant Distant Two Labeling and Circular Chromatic Number
Let G be a graph and d,d 0 be positive integers, d 0 d. An m-(d,d 0 )-circular distance two labeling is a function f : V (G) ! {0,1,2,···,m 1} such that |f(u) f(v)|m d if u and v are adjacent; and |f(u) f(v)|m d 0 if u and v are distance two apart, where |x|m := min{|x|,m| x|}. The minimum m such that there exists an m-(d,d 0 )-circular labeling for G is called the d,d0-number of G and denoted by d,d 0(G). The d,d 0-numbers for trees can be obtained by a first-fit algorithm. In this article, we completely determine the d,1-numbers for cycles. In addition, we show connections between generalized circular distance labeling and circular chromatic number.
163750
Query Decomposition: A Multiple Neighborhood Approach to Relevance Feedback Processing in Content-based Image Retrieval
Today’s Content-Based Image Retrieval (CBIR) techniques are based on the "k-nearest neighbors" (k- NN) model. They retrieve images from a single neighborhood using low-level visual features. In this model, semantically similar images are assumed to be clustered in the high-dimensional feature space. Unfortunately, no visual-based feature vector is sufficient to facilitate perfect semantic clustering; and semantically similar images with different appearances are always clustered into distinct neighborhoods in the feature space. Confinement of the search results to a single neighborhood is an inherent limitation of the k-NN techniques. In this paper we consider a new image retrieval paradigm — the Query Decomposition model - that facilitates retrieval of semantically similar images from multiple neighborhoods in the feature space. The retrieval results are the k most similar images from different relevant clusters. We introduce a prototype, and present experimental results to illustrate the effectiveness and efficiency of this new approach to content-based image retrieval.
1870907
Macro-modeling concepts for the chip electrical interface
The power delivery network is made up of passive elements in the distribution network, as well as the active transistor loads. A chip typically has three types of power supplies that require attention: core, I/O, and analog. Core circuits consist of digital circuits and have the largest current demand. In addition to all of the system issues/models for the core, modeling the I/O subsystem has the additional requirement of modeling return paths and discontinuities. The analog circuits present yet a different challenge to the macro-modeling of the supply network because they place a tight demand on supply variations. This paper presents a design methodology on how to generate macro-models of the entire chip electrical interface. This methodology can be used by the chip, package, and system designers and is being used to design high-reliability servers.
30709401
T-graphs and the channel assignment problem
Abstract T -colorings arose from the channel assignment problem in communications. Given a finite set T of non-negative integers, a T -coloring of a simple graph G is an assignment of a non-negative integer (channel) on every vertex of G , such that the difference of channels of two adjacent vertices does not fall in T . The T -span of G , denoted by sp T ( G ), is the minimum span among all possible T -colorings of G , where the span of a T -coloring is the difference between the largest and smallest channels used. This article applies T -graphs to explore the sets T that belong to these two collections: G = { T : greedy (or first-fit) algorithm provides T -spans for all complete graphs}, and E = { T : spt ( G ) = sp T ( K χ ( G ) ) for all graphs G , where χ is the chromatic number}. Given T , its T -graph has the vertex set of all non-negative integers so that two vertices are adjacent if their difference does not fall in T . We show that for any a ∈ Z + , the T -graph of aT is isomorphic to a disjoint product of a copies of the T -graph of T , where aT is the set obtained by multiplying every element of T by a . Based on this characterization, the following two results are attained. For any a ∈ Z + , T ∈ E if and only if aT ∈ E , and T ∈ G if and only if aT ∈ G . The second fact has been proven by Cozzens and Roberts (1991) from a different approach. We will completely solve the family of sets T = {0, s , s + 1,…, l } by providing a different proof of the fact T ∈ G (Tesman, 1989), and showing that T ∈ E if and only if l is a multiple of s . In addition, complete solutions for a more general family are obtained: for any a , s , l ∈ Z + with s ⩽ l , T = {0, as , a ( s + 1), a ( s + 2),…, al } ∪ A ∈ E , and T ϵ E if and only if l = ms for some m , where A ⊆{ as + 1, as + 2, …, al - 1}.
7951709
Design of bidirectional associative memories based on the perceptron training technique
Bidirectional associative memories are being used extensively for solving a variety of problems related to pattern recognition. In the present paper, a new synthesis approach is developed for bidirectional associative memories using feedback neural networks. The synthesis problem of bidirectional associative memories is formulated as a set of linear inequalities which can be solved using the perceptron training algorithm. To demonstrate the applicability of the present results a specific example is considered.