DocumentCode :
2985714
Title :
Relevance feedback techniques for image retrieval using multiple attributes
Author :
Chua, Tat-Seng ; Chu, Chun-Xin ; Kankanhalli, Mohan
Author_Institution :
Sch. of Comput., Nat. Univ. of Singapore, Singapore
Volume :
1
fYear :
1999
fDate :
36342
Firstpage :
890
Abstract :
The 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 12000 images. The results demonstrate that the proposed RF approaches and pseudo object based color model are effective
Keywords :
content-based retrieval; feature extraction; image colour analysis; learning (artificial intelligence); relevance feedback; very large databases; visual databases; RF approach; color attributes; color coherence vector; color content; content based image retrieval; feature extraction; information retrieval; large image collection; machine learning; multiple attributes; pseudo object based color model; pseudo object model; relevance feedback techniques; relevance judgments; Coherence; Content based retrieval; Data mining; Feature extraction; Feedback; Image retrieval; Information retrieval; Machine learning; Radio frequency; System testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia Computing and Systems, 1999. IEEE International Conference on
Conference_Location :
Florence
Print_ISBN :
0-7695-0253-9
Type :
conf
DOI :
10.1109/MMCS.1999.779320
Filename :
779320
Link To Document :
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