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• W2076256437 abstract "In this work, we present a new model of visual saliency by combing results from existing methods, improving upon their performance and accuracy. By fusing pre-attentive and context-aware methods, we highlight the abilities of state-of-the-art models while compensating for their deficiencies. We put this theory to the test in a series of experiments, comparatively evaluating the visual saliency maps and employing them for content-based image retrieval and thumbnail generation. We find that on average our model yields definitive improvements upon recall and f-measure metrics with comparable precisions. In addition, we find that all image searches using our fused method return more correct images and additionally rank them higher than the searches using the original methods alone." @default.
• W2076256437 created "2016-06-24" @default.
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• W2076256437 date "2015-02-27" @default.
• W2076256437 modified "2023-09-23" @default.
• W2076256437 title "Fused methods for visual saliency estimation" @default.
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• W2076256437 doi "https://doi.org/10.1117/12.2079626" @default.
• W2076256437 hasPublicationYear "2015" @default.
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