Weighting scheme for image retrieval based on bag-of-visual-words

• Published in: IET image processing Vol. 8; no. 9; pp. 509 - 518
• Main Authors: Zhu, Lei, Jin, Hai, Zheng, Ran, Feng, Xiaowen
• Format: Journal Article
• Language: English
• Published: Stevenage The Institution of Engineering and Technology 01.09.2014; Institution of Engineering and Technology; The Institution of Engineering & Technology
• Subjects: Applied sciences; bag‐of‐visual‐words; category weight mapping table; Construction; content‐based image retrieval; content‐based retrieval; document image processing; Exact sciences and technology; image classification; Image processing; image retrieval; Information theory; Information, signal and communications theory; Learning; learning (artificial intelligence); linear classifler; Mapping; max‐margin learning; query category mapping table; query image; Query processing; query specific weight; Retrieval; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Tables (data); Telecommunications and information theory; text analysis; text retrieval; visual content; visual word extraction; weight learning; weight mapping; Weighting; weighting scheme; word processing; text analysis; visual content; image classification; document image processing; text retrieval; linear classifler; content-based retrieval; weight learning; word processing; query image; query specific weight; query category mapping table; weight mapping; bag-of-visual-words; image retrieval; weighting scheme; visual word extraction; max-margin learning; learning (artificial intelligence); content-based image retrieval; category weight mapping table; Performance evaluation; Discriminant analysis; Automatic classification; Multistage method; Content based retrieval; Query; Image retrieval; Information retrieval; Mapping; Signal classification; Learning; Weighting; On line processing; Database; Off line
• ISSN: 1751-9659; 1751-9667; 1751-9667
• DOI: 10.1049/iet-ipr.2013.0375

Inspired by the success of bag-of-words in text retrieval, bag-of-visual-words and its variants are widely used in content-based image retrieval to describe visual content. Various weighting schemes have also been proposed to integrate different yet complementary visual-words. However, most of these weighting schemes tend to use fixed weight for every visual-word extracted from the query image, which may lose the discriminative information. This study presents a novel combining method which captures query-specific weights for visual-words in query image. The method mainly contains two stages. Firstly, in offline weight learning, the authors introduce a linear classifier to build a query-category mapping table, and max-margin learning to build category-weight mapping table. Query-category mapping table is used to map the query image to the most likely image class, and category-weight mapping table is used to map image class to the weights of visual-words. Secondly, in online weight mapping, the weights of visual-words are determined efficiently by looking into the pre-learned mapping tables. Experimental results on WANG database and Caltech 101 demonstrate that the proposed weighting scheme can effectively weight visual-words of query image according to their discriminative information. In addition, comparative experiments demonstrate the proposed weighting scheme can obtain higher retrieval performance than other weighting schemes.