Content-based image retrieval: approaches and trends of the new age
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Online multi-label active annotation: towards large-scale content-based video search
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Leveraging active learning for relevance feedback using an information theoretic diversity measure
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
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Concept learning in content-based image retrieval (CBIR) systems isa challenging task. This paper presents an active concept learningapproach based on mixture model to deal with the two basic aspectsof a database system: changing (image insertion or removal) natureof a database and user queries. To achieve concept learning, wedevelop a novel model selection method based on Bayesian analysisthat evaluates the consistency of hypothesized models with theavailable information. The analysis of exploitation vs. explorationin the search space helps to find optimal model efficiently.Experimental results on Corel database show the efficacy of ourapproach.