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

Content Provider	IET Digital Library
Author	Zhu, Lei Jin, Hai Zheng, Ran Feng, Xiaowen
Abstract	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.
Starting Page	509
Ending Page	518
Page Count	10
ISSN	17519659
Volume Number	8
e-ISSN	17519667
Issue Number	Issue 9, Sep (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/8/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2013.0375
Journal	IET Image Processing
Publisher Date	2014-03-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bag-of-visual-words Category Weight Mapping Table Computer Vision And Image Processing Technique Content-based Image Retrieval Content-based Retrieval Document Image Processing Document Processing Technique Image Classification Image Retrieval Information Retrieval Technique Knowledge Engineering Technique Learning in AI Linear Classifler Max-margin Learning Optical, Image And Video Signal Processing Query Category Mapping Table Query Image Query Specific Weight Text Analysis Text Retrieval Visual Content Visual Word Extraction Weight Learning Weight Mapping Weighting Scheme Word Processing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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