  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/
