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Sparse Output Coding for Scalable Visual Recognition

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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.

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Authors and Affiliations

  1. School of Computer Science, Carnegie Mellon University, Pittsburgh, USA

    Bin Zhao & Eric P. Xing

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  1. Bin Zhao
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  2. Eric P. Xing
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Correspondence to Bin Zhao.

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Communicated by Antonio Torralba and Alexei Efros.

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Zhao, B., Xing, E.P. Sparse Output Coding for Scalable Visual Recognition. Int J Comput Vis 119, 60–75 (2016). https://doi.org/10.1007/s11263-015-0839-4

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  • DOI: https://doi.org/10.1007/s11263-015-0839-4

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