Efficient sequential correspondence selection by cosegmentation

In many retrieval, object recognition and wide baseline stereo methods, correspondences of interest 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. Waldpsilas 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.