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Content-Based Image Retrieval using RBF Neural Network  

Lee, Hyoung-K (한국전자통신연구원 영상정보처리연구팀 연구원)
Yoo, Suk-I (서울대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.29, no.3, 2002 , pp. 145-155 More about this Journal
Abstract
In content-based image retrieval (CBIR), most conventional approaches assume a linear relationship between different features and require users themselves to assign the appropriate weights to each feature. However, the linear relationship assumed between the features is too restricted to accurately represent high-level concepts and the intricacies of human perception. In this paper, a neural network-based image retrieval (NNIR) model is proposed. It has been developed based on a human-computer interaction approach to CBIR using a radial basis function network (RBFN). By using the RBFN, this approach determines the nonlinear relationship between features and it allows the user to select an initial query image and search incrementally the target images via relevance feedback so that more accurate similarity comparison between images can be supported. The experiment was performed to calculate the level of recall and precision based on a database that contains 1,015 images and consists of 145 classes. The experimental results showed that the recall and level of the proposed approach were 93.45% and 80.61% respectively, which is superior than precision the existing approaches such as the linearly combining approach, the rank-based method, and the backpropagation algorithm-based method.
Keywords
content-based image retrieval; radial basis function network; linearly combining method; rank-based method; relevance feedback;
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