Extraction of Combined Features from Global/Local Statistics of Visual Words Using Relevant Operations

• MATSUKAWA Tetsu

  School of Systems and Information Engineering, University of Tsukuba

• KURITA Takio

  Department of Information Engineering, Hiroshima University

Abstract

This paper presents a combined feature extraction method to improve the performance of bag-of-features image classification. We apply 10 relevant operations to global/local statistics of visual words. Because the pairwise combination of visual words is large, we apply feature selection methods including fisher discriminant criterion and L1-SVM. The effectiveness of the proposed method is confirmed through the experiment.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E93-D (10), 2870-2874, 2010

  The Institute of Electronics, Information and Communication Engineers

Keywords

• combined feature
• bag-of-features
• feature selection
• image classification

Details

• CRID: 1390001204379596032
• NII Article ID: 10027641440
• NII Book ID: AA10826272
• DOI: 10.1587/transinf.e93.d.2870
• ISSN: 17451361
• ISSN: 09168532
• Web Site: http://www.jstage.jst.go.jp/article/transinf/E93.D/10/E93.D_10_2870/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed