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Object-level saliency detection with color attributes
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DOI：10.1016/j.patcog.2015.07.005.png)
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摘要
En 中文
Recently, saliency detection has been becoming a popular topic in computer vision. In this paper we propose an object-level saliency detection algorithm which explicitly explores bottom-up visual attention and objectness cues. Firstly, some category-independent object candidates are segmented from the image by the quantized color attributes. Then two metrics, global cues and candidate objectness, are developed to estimate the saliency in the whole image and the object candidates respectively. We use global cues to describe the focusness and spatial distribution of color attributes in the entire image. As supplementary, candidate abjectness can reveal the objectness of the object candidates. Finally, we integrate the two saliency cues to derive a saliency map of the image. By explicitly fusing candidate abjectness and global cues, our proposed method is more suitable for processing images with complex background. Experimental results on three large benchmark databases demonstrate that the proposed method achieves more favorable performance than 15 recent state-of-the-art methods in terms of three evaluation criterions. (C) 2015 Elsevier Ltd. All rights reserved.
Keyword:
Candidate objectness
Global cues
Color attributes
Saliency detection
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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