Color contrast is an important cue for image attention region detection. Extracting image regions that contain distinguishing color features is very helpful for computing the contrast of each image region. To obtain an efficient contrast map, the closure prior is firstly exploited to pick up the image regions containing distinguishing color features via connectivity detection in layered bit-planes. Secondly, the background prior is used to remove closed regions that touch image boundaries, and obtain closed region masks, in which the elements of closed regions are labeled with "1". Thirdly, a hypothesis, that a region should have big chance to be an attention region if it appears more times as a closed region in layered bit-planes, is proposed based on the contrast and closure priors. Further, the closed region masks of all bit-planes are accumulated to obtain the contrast of each connected region. Meanwhile, by taking account of the characteristics of human visual system with respect to the perception for small attention region, and visual resource allocation, several morphological filtering technologies are adopted to the key steps of contrast computing. Finally, the saliency map oriented to visual fixation estimation is generated. The experimental results show the presented detection method achieves acceptable performance compared with several state-of-the-art models.