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This paper presents a method for unsupervised scene categorization. Our method aims at two objectives: (1) automatic feature selection for different scene categories. We represent images in a heterogeneous feature space to account for the large variabilities of different scene categories. Then, we use the information projection strategy to pursue features which are both informative and discriminative,...
The objective of this paper is to parse object trajectories in surveillance video against occlusion, interruption, and background clutter. We present a spatio-temporal graph (ST-Graph) representation and a cluster sampling algorithm via deferred inference. An object trajectory in the ST-Graph is represented by a bundle of ldquomotion primitivesrdquo, each of which consists of a small number of matched...
In this paper, we present a probabilistic framework for edge and region grouping using conditional random field. Our model is built on a hybrid adjacency graph of atomic region and contour primitives. Unary and pairwise potentials that capture similarity, proximity and curvilinear continuity are defined. Similarity, for both region and edge cues, is measured by likelihood ratios learned from a human...
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