2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2009
DOI: 10.1109/cvprw.2009.5204207
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Abstract: We present a novel bottom-up

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Cited by 72 publications
(40 citation statements)
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References 19 publications
(46 reference statements)
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“…Finally, we compare our MPCA based method with four state-of-the art methods: a method that fuses optical flow and color features (OF) [17], the self-resemblance method (SF) [21], the phase discrepancy based saliency detection method (PD) [12], and a method that uses LBP features extended to temporal domain (LBP) [16].…”
Section: Methods Comparisonmentioning
confidence: 99%
“…Finally, we compare our MPCA based method with four state-of-the art methods: a method that fuses optical flow and color features (OF) [17], the self-resemblance method (SF) [21], the phase discrepancy based saliency detection method (PD) [12], and a method that uses LBP features extended to temporal domain (LBP) [16].…”
Section: Methods Comparisonmentioning
confidence: 99%
“…We compared our model against Bruce's AIM [1] [12], SUN [5,15] (2008), Seo [13,14] (2009) and finally, Jian Li [6] in 2011.The proposed RARE method correctly detects the main blobs of interest in the images. A difference with other methods is also the nature of the final saliency maps which have a high resolution (even low-pass filtered in Figure 3) compared to other methods.…”
Section: Qualitative Comparisonmentioning
confidence: 99%
“…In this paper, we model the bottom-up approach on still images. The literature [1,3,4,5,6,7,10,12,13,14,15] is very active in the field. Those models have various technical approaches even if they all derive from the same idea: they look for contrasted, rare, surprising, novel, worthy to learn, less compressible, maximizing the information areas.…”
Section: Introductionmentioning
confidence: 99%
“…The map infers the most probable locations of the subjects of the photograph according to highly distinct salient cues. Saliency by Self-Resemblance method [43] computes socalled local regression kernels, which measure the likeness of a pixel to its surroundings. Visual saliency is then computed using self-resemblance measure resulting saliency map where each pixel indicates the statistical likelihood of saliency of a feature matrix.…”
Section: Methodsmentioning
confidence: 99%