PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Spatial Priors. Driven by recent vision and graphics applications such as image segmentation and object recognition, assigning pixel-accurate saliency values to uniformly highlight foreground objects becomes increasingly critical. More often, such fine-grained saliency detection is also desired to have a fast runtime. Motivated by these, we propose a generic and fast computational framework called PISA - Pixel wise Image Saliency Aggregating complementary saliency cues based on color and structure contrasts with spatial priors holistically. Overcoming the limitations of previous methods often using homogeneous super pixel-based and color contrast-only treatment, our PISA approach directly performs saliency modeling for each individual pixel and makes use of densely overlapping, feature-adaptive observations for saliency measure computation. We further impose a spatial prior term on each of the two contrast measures, which constrains pixels rendered salient to be compact and also centered in image domain. By fusing complementary contrast measures in such a pixel wise adaptive manner, the detection effectiveness is significantly boosted. Without requiring reliable region segmentation or post-relaxation, PISA exploits an efficient edge-aware image representation and filtering technique and produces spatially coherent yet detail-preserving saliency maps. Extensive experiments on three public datasets demonstrate PISA’s superior detection accuracy and competitive runtime speed over the state-of-the-arts approaches.
References in zbMATH (referenced in 2 articles , 1 standard article )
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- Kong, Deming; Duan, Liangliang; Wu, Peiliang; Yang, Wenji: Salient region detection via feature combination and discriminative classifier (2015)
- Wang, Keze; Lin, Liang; Lu, Jiangbo; Li, Chenglong; Shi, Keyang: PISA: pixelwise image saliency by aggregating complementary appearance contrast measures with edge-preserving coherence (2015)