Title :
Relevance Feedback in Image Retrieval Based on RSVM
Author_Institution :
Dept. of Comput. Sci., Beijing Inst. of Graphic Commun., Beijing, China
Abstract :
Support vector machines (SVM) are favored for relevance feedback in content-based image retrieval by utilizing both positive and negative feedbacks. This paper uses incremental reduced support vector machines to get the support vectors and the non-support vectors, then utilizes both positive and negative feedbacks for image retrieval based on SVM. It needn´t use the results of retrieval to train SVM again as traditional method. Experimental results show that the model has good effectiveness.
Keywords :
content-based retrieval; image retrieval; relevance feedback; support vector machines; RSVM; content-based image retrieval; reduced support vector machine; relevance feedback; Computer graphics; Computer science; Content based retrieval; Image databases; Image retrieval; Information retrieval; Least squares methods; Negative feedback; Support vector machine classification; Support vector machines; -support vector machines(SVM); image retrieval; incremental reduced support vector machines (IRSVM);
Conference_Titel :
Information Engineering, 2009. ICIE '09. WASE International Conference on
Conference_Location :
Taiyuan, Shanxi
Print_ISBN :
978-0-7695-3679-8
DOI :
10.1109/ICIE.2009.230
