Submodular Object Recognition

Fan Zhu, Zhuolin Jiang, Ling Shao

Research output: Contribution to conferenceOtherpeer-review

24 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 midlevel 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 figureground 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
Event2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) - Columbus, OH, USA
Duration: 23 Jun 201428 Jun 2014


Conference2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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