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This paper presents a co-salient object detection method to find common salient regions in a set of images. We utilize deep saliency networks to transfer co-saliency prior knowledge and better capture high-level semantic information. The resulting initial co-saliency maps are enhanced by seed propagation steps over an integrated graph. The deep saliency networks are trained in a supervised manner to avoid weakly supervised online learning and exploit them not only to extract high-level features but also to produce both intra- and inter-image saliency maps. Through a refinement step, the initial co-saliency maps can uniformly highlight co-salient regions and locate accurate object boundaries. To handle input image groups inconsistent in size, we propose to pool multi-regional descriptors including both within-segment and within-group information. In addition, the integrated multilayer graph is constructed to find the regions that the previous steps may not detect by seed propagation with low-level descriptors. In this paper, we utilize the useful complementary components of high- and low-level information and several learning-based steps. Our experiments have demonstrated that the proposed approach outperforms comparable co-saliency detection methods on widely used public databases and can also be directly applied to co-segmentation tasks.

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This paper presents a backlight control algorithm for liquid crystal display devices, which considers the human visual properties that we are usually attracted to the saliencies in a scene. The saliency means regions or objects that have different contrast or color from the surrounding, and thus attract our attention, which can be measured in various ways from each pixel value and its relation with the surrounding ones. Hence, by keeping or boosting the backlight of salient regions while suppressing in others, the quality of salient regions and overall contrast are enhanced. In addition, power can be saved by backlight dimming in non‐salient regions, without loss of overall quality in terms of human visual perception. In this backlight control process, the amount of energy consumption is regulated so that the proposed method consumes less than or equal energy as before, by developing a power management algorithm based on the bit rate control strategies of MPEG2 video encoders. Precisely, the energy consumption in each backlight unit is controlled by a parameter, so that the sum of energies of overall backlight units is kept within a limit.

Statistical models of color channels have been used for the detection of skin areas. However, since the distribution of colors also change as the luminance varies, color distribution models without considering the luminance variation do not work well for the images taken under various illumination conditions. Hence we propose a new skin detection algorithm that considers the luminance value in modeling the color distribution. For implementing this idea, we need a sample of skin color in the image, which can be obtained from the face. It is noted that the faces can be detected without color feature, but by using only structural features such as eyes, mouth, etc. In our algorithm, the eye detector and elliptical boundary are used to detect the face. From the face region, joint Gaussian distributions of color components with respect to the luminance values are obtained. Then, each pixel in the image is classified into skin or non-skin using the statistics obtained from the face region. Experimental results show that the proposed skin color model outperforms the conventional methods in terms of the detection accuracy and F-score.