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Image Classification Based on Improved Spatial Pyramid Matching Model

  • Li FengEmail author
  • Xiaofeng Wang
  • Dongfang Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

Abstract

Spatial pyramid matching (SPM) uses the statistics of local features in an image sub region as a global feature. It shows good performance in terms of generic image recognition. However, the disadvantages of this method are that the constructed visual dictionary is easy to fall into a local optimal solution due to the randomness of the initial centroid of k-means and it ignores the spatial distribution of salient object in images. In this research, we propose a new clustering method that using black hole algorithm to determine the initial center of k-means when constructing a visual dictionary and making the result have globally optimal solution and less computational costs. To better distinguish the target and background in the image, we propose discriminative SPM, which is a new representation that forms the image feature as a weighted sum of features over all pyramid levels. The weights are selected by the spatial distribution of salient objects in images. The resulting feature is compact and preserves high discriminative power. Thus reducing the effect of image background on classification. As documented in the experimental results, the proposed schemes can improve the classification accuracy of image compared to the other existing methods.

Keywords

K-means Black hole algorithm Spatial pyramid Initial center Salient object Discriminative SPM 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina
  2. 2.Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time IndustrialWuhanChina

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