Visual Saliency Detection Algorithm Based On Multi-scale Region Contrast

With the appearance and development of Internet and various kinds of electronic appliances, the number of images and videos increases explosively as a novel information carrier. However, the speed of processing images and videos cannot catch up with its increasing speed. Therefore, in order to improve the ability of processing visual data for computer, the concept of visual saliency come up based on human vision system. Vision saliency is brought based on the human’s visual sensing mechanism, and can be described as the ability to attract attention for elements in the scene. The ability is quantified by the algorithm for detection of visual saliency. We can get the distribution of saliency in the image through the algorithms, and separate salient region from background for separate analysis and processing to salient region,therefore increase the processing speed for visual data.The main research of this paper mainly focuses on the visual saliency detection algorithm based on multi-scale region comparison. Based on from-bottom-to-top visual selective focus mechanism, the algorithm can detect sharp-edge and intact-content salient region with the method of multi-scale image segmentation,comparison of region color’ characters and human vision’s regular pattern. Based on above discussion, we propose the multi-scale super-pixel image segmentation algorithm and visual saliency detection algorithm based on multi-scale region comparison. The main content is listed below:(1) on the basis of the existing super-pixel segmentation algorithm, we propose the super-pixel image segmentation algorithm. At first, generate super-pixels with SLIC algorithm segmenting the image, and then cluster with the unit of super-pixel to create super-pixels in higher scale, in which the super-pixels’ character distance is presented by the distance of their color histograms, and lower the computation complexity by determining the super-pixels’ characters comparison region based on the size of super-pixel. Iteratively compute new centers and repeat clustering until theresidual error of characters satisfy the threshold. In this way, we can get multi-size and multi-scale super-pixels for the region representation in the following algorithm for visual saliency detection based on multi-scale region comparison.(2) In order to carry on more precise detection for salient target in the image, we propose an algorithm for visual saliency detection based on multi-scale region comparison. Firstly, the multi-scale super-pixel image segmentation algorithm separates the image into various numbers of super-pixel, and averages the color values in the super-pixel to subtract the color character of the super-pixel. Secondly, compute the rarity of super-pixel in single scale based on the salient character, and also compute the dispersibility of the super-pixel based on dispersibility of salient character in space and moving visual focus, and merge the rarity and dispersibility to get the sub salient diagram in this scale. Finally, generate final salient diagram by averaging the salient values of super-pixels on every scale. The result shows that taking the 1000 random images int MSRA image database, the proposed algorithm improves the accuracy of salient target recognition by 14.8%, F-Measure by 9.2%compared with existing well-performed RC(Region-Contract) algorithm. Compared with existing algorithms, the proposed algorithm improves the adaptability of the algorithm for the size of salient target and complexity of the background, reduces the disturbance of the background for salient target recognition, and has better consistency.