Bibliographic Details
| Title: |
Sparse Output Coding for Scalable Visual Recognition. |
| Authors: |
Zhao, Bin1 binzhao@andrew.cmu.edu, Xing, Eric1 epxing@cs.cmu.edu |
| Source: |
International Journal of Computer Vision. Aug2016, Vol. 119 Issue 1, p60-75. 16p. |
| Subjects: |
Object recognition (Computer vision), Probabilistic inference, Decoding algorithms, Computer programming, Empirical research |
| Abstract: |
Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-class classification, by turning high-cardinality multi-class categorization into a bit-by-bit decoding problem. Specifically, sparse output coding is composed of two steps: efficient coding matrix learning with scalability to thousands of classes, and probabilistic decoding. Empirical results on object recognition and scene classification demonstrate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
