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We propose a novel image retrieval framework for visual saliency detectionusing information about salient objects contained within bounding boxannotations for similar images. For each test image, we train a customized SVMfrom similar example images to predict the saliency values of its objectproposals and generate an external saliency map (ES) by aggregating theregional scores. To overcome limitations caused by the size of the trainingdataset, we also propose an internal optimization module which computes aninternal saliency map (IS) by measuring the low-level contrast information ofthe test image. The two maps, ES and IS, have complementary properties so wetake a weighted combination to further improve the detection performance.Experimental results on several challenging datasets demonstrate that theproposed algorithm performs favorably against the state-of-the-art methods.

Can Image Retrieval help Visual Saliency Detection?