Chengwei Chang

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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)
We demonstrate PBIR Å Å , 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 Å Å supports multimodal query and annotation in any combination(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 Active. We show that MEGA and SVM Active 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 image database.(More)
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