Chengwei Chang

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Relevance feedback is a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively learns a user's desired output or query concept by asking the user whether certain proposed images are relevant or not. For a learning algorithm to be effective, it must(More)
We describe the Perception-Based Image Retrieval (PBIR) system that we have built on our recently developed query-concept learning algorithms, MEGA and SVM<i>Active</i>. We show that MEGA and SVM<i>Active</i> can learn a complex image-query concept in a small number of user iterations (usually three to four) on a large, multi-category, high-dimensional(More)
We demonstrate PBIR-MM, an integrated system that we have built for conducting multimodal image retrieval. The system combines the strengths of content-based soft annotation (CBSA), multimodal relevance feedback through active learning, and perceptual distance formulation and indexing. PBIR-MM supports multimodal query and annotation in any combination of(More)
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