18846553
Visual Deconstruction: Recognizing Articulated Objects
We propose a deconstruction framework to recognize and find articulated objects. In particular we are interested in human arm and leg articulations.
1898904
Learning effective image metrics from few pairwise examples
We present a new approach to learning image metrics. The main advantage of our method lies in a formulation that requires only a few pairwise examples. Apparently, based on the little amount of side-information, it would take a very effective learning scheme to yield a useful image metric. Our algorithm achieves this goal by addressing two key issues. First, we establish a global-local (glocal) image representation that induces two structure-meaningful vector spaces to respectively describe the global and the local image properties. Second, we develop a metric optimization framework that finds an optimal bilinear transform to best explain the given side-information. We emphasize it is the glocal image representation that makes the use of bilinear transform more powerful. Experimental results on classifications of face images and visual tracking are included to demonstrate the contributions of the proposed method
3193257
Sparse representations for image decomposition with occlusions
We study the problem of how to detect "interesting objects" appeared in a given image, I. Our approach is to treat it as a function approximation problem based on an over-redundant basis, and also account for occlusions, where the basis superposition principle is no longer valid. Since the basis (a library of image templates) is over-redundant, there are infinitely many ways to decompose I. We are motivated to select a sparse/compact representation of I, and to account for occlusions and noise. We then study a greedy and iterative "weighted L/sup p/ Matching Pursuit" strategy, with O<p<1. We use an L/sup p/ result to compute a solution, select the best template, at each stage of the pursuit.
52838645
Representation and Self-Similarity of Shapes
Representing shapes in a compact and informative form is a significant problem for vision systems that must recognize or classify objects. We describe a compact representation model for two-dimensional (2D) shapes by investigating their self-similarities and constructing their shape axis trees (SA-trees). Our approach can be formulated as a variational one (or, equivalently, as MAP estimation of a Markov random field). We start with a 2D shape, its boundary contour, and two different parameterizations for the contour (one parameterization is oriented counterclockwise and the other clockwise). To measure its self-similarity, the two parameterizations are matched to derive the best set of one-to-one point-to-point correspondences along the contour. The cost functional used in the matching may vary and is determined by the adopted self-similarity criteria, e.g., cocircularity, distance variation, parallelism, and region homogeneity. The loci of middle points of the pairing contour points yield the shape axis and they can be grouped into a unique free tree structure, the SA-tree. By implicitly encoding the (local and global) shape information into an SA-tree, a variety of vision tasks, e.g., shape recognition, comparison, and retrieval, can be performed in a more robust and efficient way via various tree-based algorithms. A dynamic programming algorithm gives the optimal solution in O(N/sup 1/), where N is the size of the contour.
10424202
A generalized shape-axis model for planar shapes
We describe a generalized shape-axis (SA) model for representing both open and closed planar curves. The SA model is an effective way to represent shapes by comparing their self-similarities. Given a 2D shape, whether it is closed or open, we use two different parametrizations for the curve. To study the self-similarity, the two parametrizations are matched to each other via a variational framework, where the self-similarity criterion is to be defined depending on the class of shapes and human perception factors. Useful self-similarity criteria include symmetry, parallelism and convexity, etc. A match is allowed to have discontinuities, and the optimal match can be computed by a dynamic programming algorithm in O(N/sup 4/) time, where N is the size of the shape. We use a grouping process for the shape axis to construct a unique SA-tree, however, when a planar shape is open, it is possible to derive an SA-forest. The generalized SA model provides a compact and informative way for 2D shape representation.
122073
Fast Object Detection with Occlusions
We describe a new framework, based on boosting algorithms and cascade structures, to efficiently detect objects/faces with occlusions. While our approach is motivated by the work of Viola and Jones, several techniques have been developed for establishing a more general system, including (i) a robust boosting scheme, to select useful weak learners and to avoid overfitting; (ii) reinforcement training, to reduce false-positive rates via a more effective training procedure for boosted cascades; and (iii) cascading with evidence, to extend the system to handle occlusions, without compromising in detection speed. Experimental results on de- tecting faces under various situations are provided to demonstrate the performances of the proposed method.
10695800
Star-Structured High-Order Heterogeneous Data Co-clustering Based on Consistent Information Theory
Heterogeneous object co-clustering has become an important research topic in data mining. In early years of this research, people mainly worked on two types of heterogeneous data (denoted by pair-wise co-clustering); while recently more and more attention was paid to multiple types of heterogeneous data (denoted by high- order co-clustering). In this paper, we studied the high- order co-clustering of objects with star-structured interrelationship, i.e., there is a central type of objects that connects the other types of objects. Actually, this case could be a very good model for many real-world applications, such as the co-clustering of Web images, their low-level visual features, and the surrounding text. We used a tripartite graph to represent the interrelationships among different objects, and proposed a consistent information theory which generates an effective algorithm to obtain the co-clusters of different types of objects. Experiments on a Web image show that our proposed algorithm is a better choice compared with previous work on heterogeneous object co-clustering.
1735972
Shot reconstruction degree: a novel criterion for key frame selection
Abstract In this paper, the authors treat the key frame extraction problem from a viewpoint of shot reconstruction. As a result, a novel criterion called shot reconstruction degree (SRD) is proposed for key frame selection based on the degree of retaining motion dynamics of a video shot. Compared with the widely used fidelity criterion, the key frame set produced by SRD can better capture the detailed dynamics of the shot. Using the new SRD criterion, a novel inflexion-based key frame selection algorithm is developed. Simulation results show that the new algorithm results in good performance in terms of both fidelity and shot reconstruction degree.
41574260
Adaptive motion tracking for fast block motion estimation
In this paper, an adaptive motion tracking method is proposed to provide an accurate predictive motion vector for the block matching algorithm to locate the search origin by exploiting the high motion correlation in both the temporal and spatial domain. Simulation results show that the proposed technique can greatly enhance the performance of the existing fast block matching algorithms.
9484829
Fast Large-Scale Spectral Clustering by Sequential Shrinkage Optimization
In many applications, we need to cluster large-scale data objects. However, some recently proposed clustering algorithms such as spectral clustering can hardly handle large-scale applications due to the complexity issue, although their effectiveness has been demonstrated in previous work. In this paper, we propose a fast solver for spectral clustering. In contrast to traditional spectral clustering algorithms that first solve an eigenvalue decomposition problem, and then employ a clustering heuristic to obtain labels for the data points, our new approach sequentially decides the labels of relatively well-separated data points. Because the scale of the problem shrinks quickly during this process, it can be much faster than the traditional methods. Experiments on both synthetic data and a large collection of product records show that our algorithm can achieve significant improvement in speed as compared to traditional spectral clustering algorithms.
20168507
An experimental study on large-scale web categorization
Taxonomies of the Web typically have hundreds of thousands of categories and skewed category distribution over documents. It is not clear whether existing text classification technologies can perform well on and scale up to such large-scale applications. To understand this, we conducted the evaluation of several representative methods (Support Vector Machines, k-Nearest Neighbor and Naive Bayes) with Yahoo! taxonomies. In particular, we evaluated the effectiveness/efficiency tradeoff in classifiers with hierarchical setting compared to conventional (flat) setting, and tested popular threshold tuning strategies for their scalability and accuracy in large-scale classification problems.