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Proceedings Paper

Fused methods for visual saliency estimation
Author(s): Amanda S. Danko; Siwei Lyu
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Paper 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.

Paper Details

Date Published: 27 February 2015
PDF: 11 pages
Proc. SPIE 9405, Image Processing: Machine Vision Applications VIII, 94050Z (27 February 2015); doi: 10.1117/12.2079626
Show Author Affiliations
Amanda S. Danko, Univ. at Albany, SUNY (United States)
Siwei Lyu, Univ. at Albany, SUNY (United States)

Published in SPIE Proceedings Vol. 9405:
Image Processing: Machine Vision Applications VIII
Edmund Y. Lam; Kurt S. Niel, Editor(s)

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