Efficient sequential correspondence selection by cosegmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Jan CechMichal Perdoch

Abstract

In many retrieval, object recognition, and wide-baseline stereo methods, correspondences of interest points (distinguished regions) are commonly established by matching compact descriptors such as SIFTs. We show that a subsequent cosegmentation process coupled with a quasi-optimal sequential decision process leads to a correspondence verification procedure that 1) has high precision (is highly discriminative), 2) has good recall, and 3) is fast. The sequential decision on the correctness of a correspondence is based on simple statistics of a modified dense stereo matching algorithm. The statistics are projected on a prominent discriminative direction by SVM. Wald's sequential probability ratio test is performed on the SVM projection computed on progressively larger cosegmented regions. We show experimentally that the proposed sequential correspondence verification (SCV) algorithm significantly outperforms the standard correspondence selection method based on SIFT distance ratios on challenging matching problems.

References

Nov 11, 2003·IEEE Transactions on Medical Imaging·Charles V StewartBadrinath Roysam
Oct 22, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Krystian Mikolajczyk, Cordelia Schmid
Sep 13, 2007·IEEE Transactions on Pattern Analysis and Machine Intelligence·Gehua YangChia-Ling Tsai

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Citations

Nov 6, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Hsin-Yi ChenBing-Yu Chen
Apr 7, 2010·Medical Image Analysis·Michal Sofka, Charles V Stewart

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