@InProceedings{Cech-CVPR-2008,
    author = {Jan {\v C}ech, Ji{\v r}{\' i} Matas, Michal Per{\v d}och},
    title = {Efficient Sequential Correspondence Selection by Cosegmentation},
    booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
    year = 2008,
    month = {June},
    venue = {Anchorage, Alaska, USA},
    keywords = {correspondence, verification, dense stereo, growing, sequential decison, learning, SVM, wide-baseline stereo, image retrieval, SIFT, RANSAC},
    annote = {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.},
  psurl       = {<a href="ftp://cmp.felk.cvut.cz/pub/cmp/articles/cech/Cech-CVPR-2008.pdf">[PDF]</a> },
  ISBN = {978-1-4244-2243-2},
  publisher = {Omnipress},
  address =  {Madison, WI, USA},
}