Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/39019
Title: Relevance feedback techniques for image retrieval using multiple attributes
Authors: Chua, Tat-Seng 
Chu, Chun-Xin
Kankanhalli, Mohan 
Issue Date: 1999
Source: Chua, Tat-Seng,Chu, Chun-Xin,Kankanhalli, Mohan (1999). Relevance feedback techniques for image retrieval using multiple attributes. International Conference on Multimedia Computing and Systems -Proceedings 1 : 890-894. ScholarBank@NUS Repository.
Abstract: This paper proposes a relevance feedback (RF) approach to content-based image retrieval using multiple attributes. The proposed approach has been applied to images' text and color attributes. In order to ensure that meaningful features are extracted, a pseudo object model based on color coherence vector has been adopted to model color content. The RF approach employs techniques developed in the fields of information retrieval and machine learning to extract pertinent features from each of the attributes. It then uses the user's relevance judgments to estimate the importance of different attributes in an integrated content-based image retrieval. The system developed has been tested on a large image collection containing over 12,000 images. The results demonstrate that the proposed RF approaches and pseudo-object based color model are effective.
Source Title: International Conference on Multimedia Computing and Systems -Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/39019
Appears in Collections:Staff Publications

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