Efficient Sequential Correspondence Selection by Cosegmentation

In many retrieval, object recognition and wide baseline stereo methods, correspondences of interest points (distinguished regions, transformation covariant points) are established possibly sublinearly by matching a compact descriptor such as SIFT. We show that a subsequent cosegmentation process coupled with a quasi-optimal sequential decision process leads to a correspondence verification procedure that has (i) high precision (is highly discriminative) (ii) good recall and (iii) is fast. The sequential decision on the correctness of a correspondence is based on trivial attributes of a modified dense stereo matching algorithm. The attributes are projected on a prominent discriminative direction by SVM. Wald's sequential probability ratio test is performed for SVM projection computed on progressively larger co-segmented regions. Experimentally we show that the process significantly outperforms the standard correspondence selection process based on SIFT distance ratios on challenging matching problems.

RANSAC, correspondence, SVM, image retrieval, wide-baseline stereo, sequential decison, SIFT, verification, learning, dense stereo, growing