articleJun 1, 2010Closed access

Locality-constrained Linear Coding for image classification

University of Illinois Urbana-Champaign · NEC (United States)

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Abstract

The traditional SPM approach based on bag-of-features (BoF) requires nonlinear classifiers to achieve good image classification performance. This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM. LLC utilizes the locality constraints to project each descriptor into its local-coordinate system, and the projected coordinates are integrated by max pooling to generate the final representation. With linear classifier, the proposed approach performs remarkably better than the traditional nonlinear SPM, achieving state-of-the-art performance on several benchmarks. Compared with the sparse coding strategy [22], the…

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Authors

6

Topics & keywords

Keywords
  • Locality
  • Computer science
  • Coding (social sciences)
  • Pooling
  • Artificial intelligence
  • Nonlinear system
  • Algorithm
  • Classifier (UML)
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