Integrating Multiple Global and Local Features by Product Sparse Coding for Image Retrieval

• TIAN Li

  School of Foreign Studies, Xi'an Jiaotong University

• JIA Qi

  School of Foreign Studies, Xi'an Jiaotong University

• KAMATA Sei-ichiro

  Graduate School of Information, Production and Systems, Waseda University

Abstract

In this study, we propose a simple, yet general and powerful framework of integrating multiple global and local features by Product Sparse Coding (PSC) for image retrieval. In our framework, multiple global and local features are extracted from images and then are transformed to Trimmed-Root (TR)-features. After that, the features are encoded into compact codes by PSC. Finally, a two-stage ranking strategy is proposed for indexing in retrieval. We make three major contributions in this study. First, we propose TR representation of multiple image features and show that the TR representation offers better performance than the original features. Second, the integrated features by PSC is very compact and effective with lower complexity than by the standard sparse coding. Finally, the two-stage ranking strategy can balance the efficiency and memory usage in storage. Experiments demonstrate that our compact image representation is superior to the state-of-the-art alternatives for large-scale image retrieval.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E99.D (3), 731-738, 2016

  The Institute of Electronics, Information and Communication Engineers

Keywords

• image retrieval
• image representation
• Trimmed-Root (TR)-feature
• Product Sparse Coding (PSC)
• ranking strategy

Details

• CRID: 1390282679354155136
• NII Article ID: 130005131820
• DOI: 10.1587/transinf.2015edp7337
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E99.D/3/E99.D_2015EDP7337/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed