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problem based on an over-redundant basis, and also account for occlusions, where the basis superposition principle is no longer valid.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.
1579121
Representation and self-similarity of shapes
Representing shapes is a significant problem for vision systems that must recognize or classify objects. We derive a representation for a given shape by investigating its self-similarities, and constructing its shape axis (SA) and shape axis tree (SA-tree). We start with a shape, its boundary contour, and two different parameterizations for the contour. To measure its self-similarity we consider matching pairs of points (and their tangents) along the boundary contour, i.e., matching the two parameterizations. The matching, of self-similarity criteria may vary, e.g., co-circularity, parallelism, distance, region homogeneity. The loci of middle points of the pairing contour points are the shape axis and they can be grouped into a unique tree graph, the SA-tree. The shape axis for the co-circularity criteria is compared to the symmetry axis. An interpretation in terms of object parts is also presented.
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.
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
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.
6173141
Approximate tree matching and shape similarity
We present a framework for 2D shape contour (silhouette) comparison that can account for stretchings, occlusions and region information. Topological changes due to the original 3D scenarios and articulations are also addressed. To compare the degree of similarity between any two shapes, our approach is to represent each shape contour with a free tree structure derived from a shape axis (SA) model, which we have recently proposed. We then use a tree matching scheme to find the best approximate match and the matching cost. To deal with articulations, stretchings and occlusions, three local tree matching operations, merge, cut, and merge-and-cut, are introduced to yield optimally approximate matches, which can accommodate not only one-to-one but many-to-many mappings. The optimization process gives guaranteed globally optimal match efficiently. Experimental results on a variety of shape contours are provided.
10898668
Level-Based Link Analysis
In order to get high-quality web pages, search engines often resort retrieval pages by their ranks. The rank is a kind of measurement of importance of pages. Famous ranking algorithms, including PageRank and HITS, make use of hyperlinks to compute the importance. Those algorithms consider all hyperlinks identically in sense of recommendation. However, we find that the World Wide Web is actually organized with the natural multi-level structure. Benefiting from the level properties of pages, we can describe the recommendation of hyperlinks more reasonably and precisely. With this motivation, a new level-based link analysis algorithm is proposed in this paper. In the proposed algorithm, the recommendation weight of each hyperlink is computed with the level properties of its two endings. Experiments on the topic distillation task of TREC2003 web track show that our algorithm can evidently improve searching results as compared to previous link analysis methods.
18011307
Subspace Clustering and Label Propagation for Active Feedback in Image Retrieval
In recent years, relevance feedback has been studied extensively as a way to improve performance of content-based image retrieval (CBIR). However, since users are usually unwilling to provide many feedbacks, the insufficiency of the training samples limited the success of relevance feedback. To tackle this problem, we propose two coupled algorithms: (i) overlapped subspace clustering to select representative images for user’s feedback; and (ii) multi-subspace label propagation to include unlabeled data in the training process. As these two algorithms are both working on sub feature spaces of the image database, they can not only deal with the insufficient training samples but also well capture the user’s attention during the retrieval process. Experimental results on a large database of general-purposed images demonstrated the high effectiveness of our proposed algorithms.
1733796
Event detection from evolution of click-through data
Previous efforts on event detection from the web have focused primarily on web content and structure data ignoring the rich collection of web log data. In this paper, we propose the first approach to detect events from the click-through data, which is the log data of web search engines. The intuition behind event detection from click-through data is that such data is often event-driven and each event can be represented as a set ofquery-page pairs that are not only semantically similar but also have similar evolution pattern over time. Given the click-through data, in our proposed approach, we first segment it into a sequence of bipartite graphs based on theuser-defined time granularity. Next, the sequence of bipartite graphs is represented as a vector-based graph, which records the semantic and evolutionary relationships between queries and pages. After that, the vector-based graph is transformed into its dual graph, where each node is a query-page pair that will be used to represent real world events. Then, the problem of event detection is equivalent to the problem of clustering the dual graph of the vector-based graph. The clustering process is based on a two-phase graph cut algorithm. In the first phase, query-page pairs are clustered based on thesemantic-based similarity such that each cluster in the result corresponds to a specific topic. In the second phase, query-page pairs related to the same topic are further clustered based on the evolution pattern-based similarity such that each cluster is expected to represent a specific event under the specific topic. Experiments with real click-through data collected from a commercial web search engine show that the proposed approach produces high quality results.
9438831
Dynamic selection and effective compression of key frames for video abstraction
This paper reports on a new key frame based video abstraction method. With our method, a video sequence is first segmented into a number of video shots. Several key frames are selected in each shot using a dynamic selection technique. For these key frames, a motion-based clustering algorithm is applied so that key frames in the same cluster are alike in sense of motion compensation error, while those from different clusters are quit dissimilar. Then a novel cluster-based coding scheme is developed for efficient representation of the key frames. Simulations show that the proposed method can select key frames according to the dynamics of a video sequence and abstract the video with different levels of scalability.
21449211
Effective Feature Extraction for Play Detection in American Football Video
The fact that a typical broadcast can last over 3 hours for a game of 60 minutes makes video summarization of American football games most desirable. In this paper, we present several feature extraction methods for play detection in American football video. Wavelet based motion analysis is used to extract the trend component from the noisy motion vectors; a hybrid field-color model detects field area with both high accuracy and fast speed; and a prior knowledge driven line detection method uses the court information to estimate miss-detections. Based on the so-extracted features, a boosting chain is used for feature selection and decision making. Tested on large-size video data, the detection performance of our work is very promising.