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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,...
This paper presents a framework of layered graph matching for integrating graph partition and matching. The objective is to find an unknown number of corresponding graph structures in two images. We extract discriminative local primitives from both images and construct a candidacy graph whose vertices are matching candidates (i.e., a pair of primitives) and whose edges are either negative for mutual...
Many vision tasks are posed as either graph partitioning (coloring) or graph matching (correspondence) problems. The former include segmentation and grouping, and the latter include wide baseline stereo, large motion, object tracking and recognition. In this paper, we present an integrated solution for both graph matching and graph partition using an effective sampling algorithm in a Bayesian framework...
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