Relevant Data Expansion for Learning Concept Drift from Sparsely Labeled Data
IEEE Transactions on Knowledge and Data Engineering
A Unified Log-Based Relevance Feedback Scheme for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Incorporating multiple SVMs for automatic image annotation
Pattern Recognition
Fuzzy SVM for content-based image retrieval: a pseudo-label support vector machine framework
IEEE Computational Intelligence Magazine
An interactive approach for CBIR using a network of radial basis functions
IEEE Transactions on Multimedia
A memory learning framework for effective image retrieval
IEEE Transactions on Image Processing
Learning a semantic space from user's relevance feedback for image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Query feedback for interactive image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Image retrieval using transaction-based and SVM-based learning in relevance feedback sessions
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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We propose a fuzzy combined learning approach to construct a relevance feedback-based content-based image retrieval (CBIR) system for efficient image search. Our system uses a composite short-term and long-term learning approach to learn the semantics of an image. Specifically, the short-term learning technique applies fuzzy support vector machine (FSVM) learning on user labeled and additional chosen image blocks to learn a more accurate boundary for separating the relevant and irrelevant blocks at each feedback iteration. The long-term learning technique applies a novel semantic clustering technique to adaptively learn and update the semantic concepts at each query session. A predictive algorithm is also applied to find images most semantically related to the query based on the semantic clusters generated in the long-term learning. Our extensive experimental results demonstrate the proposed system outperforms several state-of-the-art peer systems in terms of both retrieval precision and storage space.