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Image Classification Based on Weight Adjustment before Feature Pooling

  • Shaokun Feng
  • Hongtao Lu
  • Lei Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)

Abstract

In image classification based on Bag-of-Features(BoF), the Locality-constrained Linear Coding (LLC) is a successful implementation, which is a more effective coding scheme compared with the traditional vector quantization(VQ) coding. Although, to achieve the best performance, max pooling scheme is chosen in the SPM layer, much of the spatial information is still lost during the pooling step, because all the coded descriptors are given the same importance to obtain the final representation. In this paper, we propose a new scheme that makes full use of spatial structure information to readjust their relative weights red and thus give some descriptors more chances to appear in the final feature vector more than others. Experiments of image classification on benchmark datasets show that the proposed method outperforms the LLC method.

Keywords

Image Classification Weighting Adjustment Feature Pooling Weight Map 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shaokun Feng
    • 1
  • Hongtao Lu
    • 1
  • Lei Huang
    • 1
  1. 1.MOE-Microsoft Laboratory for Intelligent Computing and Intelligent, Systems Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiP.R. China

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