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)
Brown adipose tissue (BAT) activation and subcutaneous white fat browning are essential components of the thermogenic response to cold stimulus in mammals. microRNAs have been shown to regulate both processes in cis. Here, we identify miR-32 as a BAT-specific super-enhancer-associated miRNA in mice that is selectively expressed in BAT and further(More)
We demonstrate PBIR , 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 supports multimodal query and annotation in any combination of its(More)
We describe the Perception-Based Image Retrieval (PBIR) system that we have built on our recently developed query-concept learning algorithms, MEGA and SVMActive. We show that MEGA and SVMActive can learn a complex image-query concept in a small number of user iterations (usually three to four) on a large, multicategory, high-dimensional image database.
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