Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Implementing Relevance Feedback as Convolutions of Local Neighborhoods on Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Relevance Feedback Decision Trees in Content-Based Image Retrieval
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Multimedia Information Retrieval and Management: Technological Fundamentals and Applications
Multimedia Information Retrieval and Management: Technological Fundamentals and Applications
Query by example using invariant features from the double dyadic dual-tree complex wavelet transform
Proceedings of the ACM International Conference on Image and Video Retrieval
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Relevance feedback has been shown to be a very effective tool for enhancing retrieval results in text retrieval. In recent years, the relevance feedback scheme has been applied to Content-Based Image Retrieval (CBIR) and effective results have been obtained. However, most of the conventional feedback process has the problem that updating of metric space is hard to understand visually. In this paper, we propose a CBIR algorithm using Self-Organizing Map (SOM) with visual relevance feedback scheme. Then a pre-learning algorithm in the visual relevance feedback is proposed for constructing user-dependent metric space. We show the effectiveness of the proposed technique by subjective evaluation experiments.