Title :
A Relevance Feedback Algorithm Based on SVM Model´s Dynamic Adjusting for Image Retrieval
Author :
Zhou, Yihua ; Shi, Weimin ; Duan, Lijuan ; Niu, Cuiying
Author_Institution :
Beijing Univ. of Technol., Beijing
Abstract :
A fixed SVM model setting is not suitable for the evolvement of the pattern of user´s interest. In this paper a relevance feedback algorithm based on SVM model´s dynamic adjusting for image retrieval is presented. In this algorithm, there is no need to fix the model´s parameters beforehand, and the parameters of SVM model will be automatically adjusted corresponding to the changing of the training samples. Experimental results show the proposed algorithm outperformed other algorithms with fixed model´s parameter.
Keywords :
image retrieval; relevance feedback; support vector machines; SVM; image retrieval; relevance feedback algorithm; support vector machine; Computational intelligence; Computer security; Educational institutions; Feedback; Image retrieval; Labeling; Project management; Statistical learning; Support vector machine classification; Support vector machines;
Conference_Titel :
Computational Intelligence and Security Workshops, 2007. CISW 2007. International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-0-7695-3073-4
DOI :
10.1109/CISW.2007.4425493
