Fused methods for visual saliency estimation

Amanda S. Danko; Siwei Lyu

doi:10.1117/12.2079626

27 February 2015 Fused methods for visual saliency estimation

Amanda S. Danko, Siwei Lyu

Proceedings Volume 9405, Image Processing: Machine Vision Applications VIII; 94050Z (2015) https://doi.org/10.1117/12.2079626
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

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.

Citation Download Citation

Amanda S. Danko and Siwei Lyu "Fused methods for visual saliency estimation", Proc. SPIE 9405, Image Processing: Machine Vision Applications VIII, 94050Z (27 February 2015); https://doi.org/10.1117/12.2079626

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