DocumentCode :
3114515
Title :
An Image Clustering and Retrieval Framework Using Feedback-Based Integrated Region Matching
Author :
Zhou, Liping ; Zhang, Chengcui ; Wan, Wen ; Birch, Jeffrey ; Chen, Wei-Bang
Author_Institution :
Dept. of Comput. & Inf. Sci., Univ. of Alabama at Birmingham, Birmingham, AL, USA
fYear :
2009
fDate :
13-15 Dec. 2009
Firstpage :
596
Lastpage :
601
Abstract :
Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user´s retrieval target especially when more than one object of interest is involved in the retrieval. In this paper, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in reflecting users´ retrieval interests in object-based image retrieval.
Keywords :
image matching; image retrieval; pattern clustering; relevance feedback; visual databases; feedback-based image clustering framework; feedback-based integrated region matching; image database; integrated image region matching; object-based image retrieval systems; relevance feedback; single object matching; user retrieval target; Clustering algorithms; Feature extraction; Feedback; Genetic algorithms; Image databases; Image retrieval; Image segmentation; Information retrieval; Machine learning; Machine learning algorithms; clustering algorithm; information retrieval; machine learning; query reweighting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications, 2009. ICMLA '09. International Conference on
Conference_Location :
Miami Beach, FL
Print_ISBN :
978-0-7695-3926-3
Type :
conf
DOI :
10.1109/ICMLA.2009.131
Filename :
5381397
Link To Document :
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