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This paper presents an inter-query semantic learning approach for image retrieval with relevance feedback. The proposed system combines the kernel biased discriminant analysis (KBDA) based low-level learning and semantic log file (SLF) based high-level learning to achieve high retrieval accuracy after the first iteration. User's relevance feedback is utilized for updating both low-level and highlevel features of the query image. Extensive experiments demonstrate our system outperforms three peer systems.doi:10.1109/icassp.2010.5495405 dblp:conf/icassp/FechserCQ10 fatcat:6i7oqxvhojdklnvlaq2hd4f6xy