Submodular Object Recognition

Fan Zhu, Zhuolin Jiang, Ling Shao

Research output: Contribution to conferencePaper

26 Citations (Scopus)


We present a novel object recognition framework based on multiple figure-ground hypotheses with a large object spatial support, generated by bottom-up processes and mid-level cues in an unsupervised manner. We exploit the benefit of regression for discriminating segments' categories and qualities, where a regressor is trained to each category using the overlapping observations between each figure-ground segment hypothesis and the ground-truth of the target category in an image. Object recognition is achieved by maximizing a submodular objective function, which maximizes the similarities between the selected segments (i.e., facility locations) and their group elements (i.e., clients), penalizes the number of selected segments, and more importantly, encourages the consistency of object categories corresponding to maximum regression values from different category-specific regressors for the selected segments. The proposed framework achieves impressive recognition results on three benchmark datasets, including PASCAL VOC 2007, Caltech-101 and ETHZ-shape.
Original languageEnglish
Publication statusPublished - Jun 2014
EventCVPR 2014 - IEEE Conference on Computer Vision and Pattern Recognition - Columbus, Ohio
Duration: 1 Jun 2014 → …


ConferenceCVPR 2014 - IEEE Conference on Computer Vision and Pattern Recognition
Period1/06/14 → …


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