NDLI: An Adaptive Fuzzy Clustering Method for Query-by-Multiple-Example Image Retrieval

Content Provider	IEEE Xplore Digital Library
Author	Haifeng Zhao Wenhua Jia
Copyright Year	2008
Description	Author affiliation: Sch. of Electron. Eng. & Comput. Sci., Peking Univ., Beijing (Haifeng Zhao; Wenhua Jia)
Abstract	Content-based image retrieval (CBIR) methods usually adopt color, texture and structure as image feature vector. Recent research indicates that since these features are not exactly associated with image semantic meaning, query-by-one-example (QBOE), which means to query with only one image, usually is insufficient to achieve good performance. Thus, query-by-multiple-examples (QBME) methods are introduced and applied in many content-based image retrieval systems. However, how to maximize major features and minimize minor ones of these inputs while matching could influence retrieval results significantly. Some previous researchers divided input images to groups according to their relevance to desired image class. They kept important features of relevant group and discard obvious features of irrelevant group while matching. These methods did improve the retrieval results but have defects in some situations. This paper presents an improved QBME system which optimizes retrieval result. It adopts clustering method to cope with the defects of previous methods. Uniquely, to decrease the complexity of user input and reduce user-computer interaction, a modified fuzzy clustering algorithm will be introduced. It not only presents good performance but also requires no parameters from users. In this article, both the defects of previous methods and new methods to cope with them will be explained in detail.
Starting Page	997
Ending Page	1004
File Size	1022305
Page Count	8
File Format	PDF
ISBN	9780769531229
DOI	10.1109/SITIS.2007.45
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive fuzzy clustering Image color analysis Content-based image retrieval (CBIR) Clustering methods Image matching Image retrieval Hybrid fiber coaxial cables Feature extraction Distance measurement Frequency control
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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