A learning state-space model for image retrieval
EURASIP Journal on Applied Signal Processing
Content-based image retrieval with the normalized information distance
Computer Vision and Image Understanding
Analyzing the efficacy of using digital ink devices in a learning environment
Multimedia Tools and Applications
Hidden annotation for image retrieval with long-term relevance feedback learning
Pattern Recognition
Region-based semantic similarity propagation for image retrieval
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
An application of swarm intelligence to distributed image retrieval
Information Sciences: an International Journal
Topic modelling of clickthrough data in image search
Multimedia Tools and Applications
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This paper proposes a novel view of the information generated by relevance feedback. Latent semantic analysis is adapted to this view to extract useful inter-query information. The view presented in this paper is that the fundamental vocabulary of the system is the images in the database and that relevance feedback is a document whose words are the images. A relevance feedback document contains the intra-query information which expresses the semantic intent of the user over that query. The inter-query information then takes the form of a collection of documents which can be subjected to latent semantic analysis. An algorithm toquery the latent semantic index is presented and evaluated against real data sets.