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We present a new approach, called local discriminant embedding (LDE), to manifold learning and pattern classification. In our framework, the neighbor and class relations of data are used to construct the embedding for classification problems. The proposed algorithm learns the embedding for the submanifold of each class by solving an optimization problem.(More)
We describe a tracking algorithm to address the interactions among objects, and to track them individually and confidently via a static camera. It is achieved by constructing an invariant bipartite graph to model the dynamics of the tracking process, of which the nodes are classified into objects and profiles. The best match of the graph corresponds to an(More)
Learning the user's semantics for CBIR involves two different sources of information: the similarity relations entailed by the content-based features, and the relevance relations specified in the feedback. Given that, we propose an <i>augmented relation embedding</i> (ARE) to map the image space into a <i>semantic manifold</i> that faithfully grasps the(More)
We propose a color-based tracking framework that infers alternately an object's configuration and good color features via particle filtering. The tracker adaptively selects discriminative color features that well distinguish foregrounds from backgrounds. The effectiveness of a feature is weighted by the Kullback-Leibler observation model, which measures(More)
This paper presents a new algorithm to solve the problem of co-saliency detection, i.e., to find the common salient objects that are present in both of a pair of input images. Unlike most previous approaches, which require correspondence matching , we seek to solve the problem of co-saliency detection under a preattentive scheme. Our algorithm does not need(More)
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.(More)
Optimization methods based on iterative schemes can be divided into two classes: line-search methods and trust-region methods. While line-search techniques are commonly found in various vision applications, not much attention is paid to trust-region ones. Motivated by the fact that line-search methods can be considered as special cases of trust-region(More)