Driven by recent vision and graphics applications such as image segmentation and object recognition, computing pixel-accurate saliency values to uniformly highlight foreground objects becomes increasingly important. In this paper, we propose a unified framework called PISA, which stands for Pixelwise Image Saliency Aggregating various bottom-up cues and priors. It generates spatially coherent yet detail-preserving, pixel-accurate and fine-grained saliency, and overcomes the limitations of previous methods which use homogeneous superpixel-based and color only treatment. PISA aggregates multiple saliency cues in a global context such as complementary color and structure contrast measures with their spatial priors in the image domain. The saliency confidence is further jointly modeled with a neighborhood consistence constraint into an energy minimization formulation, in which each pixel will be evaluated with multiple hypothetical saliency levels. Instead of using global discrete optimization methods, we employ the cost-volume filtering technique to solve our formulation, assigning the saliency levels smoothly while preserving the edge-aware structure details. In addition, a faster version of PISA is developed using a gradient-driven image sub-sampling strategy to greatly improve the runtime efficiency while keeping comparable detection accuracy. Extensive experiments on a number of public datasets suggest that PISA convincingly outperforms other state-of-the-art approaches. In addition, with this work we also create a new dataset containing $800$ commodity images for evaluating saliency detection. The dataset and source code of PISA can be downloaded at http://vision.sysu.edu.cn/project/PISA/

本文提出了一种称为PISA的统一框架，该框架采用各种自底向上的线索和先验条件聚合像素精确的显着性值，并在全局上下文中聚合多个显着性线索。

PISA：通过聚合互补外观对比度度量和边缘保留相干性的像素级图像显著性