GAL selects images for user labeling by the estimated impact of each candidate on the retrieval classifier, using a greedy batch scheme, and reports improved retrieval accuracy over prior active learning methods on four benchmarks.
Relevance feedback for enhancing content based image retrieval and automatic prediction of semantic image features: Application to bone tumor radiographs
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Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval
GAL selects images for user labeling by the estimated impact of each candidate on the retrieval classifier, using a greedy batch scheme, and reports improved retrieval accuracy over prior active learning methods on four benchmarks.