Decomposed learning for joint object segmentation and categorization

Yi Hsuan Tsai, Jimei Yang, Ming Hsuan Yang

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

We present a learning algorithm for joint object segmentation and categorization that decomposes the original problem into two sub-tasks and admits their bidirectional interaction. In the first stage, in order to decompose output space, we train category-specific segmentation models to generate figure-ground hypotheses. In the second stage, by taking advantage of object figure-ground information, we train a multi-class segment-based categorization model to determine the object class. A re-ranking strategy is then applied to classified segments to obtain the final category-level segmentation results. Experiments on the Graz-02 and Caltech-101 datasets show that the proposed algorithm performs favorably against the state-of-the-art methods.

Original languageEnglish
DOIs
Publication statusPublished - 2013 Jan 1
Event2013 24th British Machine Vision Conference, BMVC 2013 - Bristol, United Kingdom
Duration: 2013 Sep 92013 Sep 13

Conference

Conference2013 24th British Machine Vision Conference, BMVC 2013
CountryUnited Kingdom
CityBristol
Period13/9/913/9/13

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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    Tsai, Y. H., Yang, J., & Yang, M. H. (2013). Decomposed learning for joint object segmentation and categorization. Paper presented at 2013 24th British Machine Vision Conference, BMVC 2013, Bristol, United Kingdom. https://doi.org/10.5244/C.27.80