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Journal of Imaging
We propose a novel multiple query retrieval approach, named weight-learner, which relies on visual feature discrimination to estimate the distances between the query images and images in the database. For each query image, this discrimination consists of learning, in an unsupervised manner, the optimal relevance weight for each visual feature/descriptor. These feature relevance weights are designed to reduce the semantic gap between the extracted visual features and the user's high-leveldoi:10.3390/jimaging6010002 pmid:34460641 fatcat:smvddgbhqvdpfd4oiopu2irihy