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Scene image classification using locality-constrained linear coding based on histogram intersection

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Abstract

Recently linear Spatial Pyramid Matching (SPM) method based on sparse coding has achieved great success in image classification. The raise of Locality-constrained Linear Coding (LLC) proves the importance of locality. In this paper, we propose an improved feature coding scheme called Locality-constrained Linear Coding Based on Histogram Intersection (HILLC). HILLC uses histogram intersection to describe the distance between feature vector and codebook. For each feature vector, search the KNN nearest neighbors to construct a local codebook. Compared with LLC, HILLC can obtain more robust codes. Experimental results demonstrate that our proposed method outperforms other related coding methods.

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Acknowledgements

This project is partly supported by NSF of China (61202134, 31671006), the Natural Science Foundation of Jiangsu Province (No.BK20140638, BK2012437).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Haiyan Chen, Huan Wang & Chunxia Zhao

  2. School of Automation, Southeast University, Nanjing, 210096, China

    Ke Xie

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  1. Haiyan Chen
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  3. Huan Wang
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Correspondence to Huan Wang.

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Chen, H., Xie, K., Wang, H. et al. Scene image classification using locality-constrained linear coding based on histogram intersection. Multimed Tools Appl 77, 4081–4092 (2018). https://doi.org/10.1007/s11042-017-4830-7

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