Supervised Class Graph Preserving Hashing for Image Retrieval and Classification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10132)

Abstract

With the explosive growth of data, hashing-based techniques have attracted significant attention due to their efficient retrieval and storage reduction ability. However, most hashing methods do not have the ability of predicting the labels directly. In this paper, we propose a novel supervised hashing approach, namely Class Graph Preserving Hashing (CGPH), which can well incorporate label information into hashing codes and classify the samples with binary codes directly. Specifically, CGPH learns hashing functions by ensuring label consistency and preserving class graph similarity among hashing codes simultaneously. Then, it learns effective binary codes through orthogonal transformation by minimizing the quantization error between hashing function and binary codes. In addition, an iterative method is proposed for the optimization problem in CGPH. Extensive experiments on two large scale real-world image data sets show that CGPH outperforms or is comparable to state-of-the-art hashing methods in both image retrieval and classification tasks.

Keywords

Hashing Image retrieval Image classification Similarity search 

Notes

Acknowledgments

This work was partially supported by National Natural Science Foundation of China (61173068, 61573212, 91546203), Program for New Century Excellent Talents in University of the Ministry of Education, Independent Innovation Foundation of Shandong Province (2014CGZH1106), Key Research and Development Program of Shandong Province (2016GGX101044, 2015GGE27033).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina

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