国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

To overcome the disadvantages of existing saliency models in saliency detection, a novel object-based attention model is presented to predict visual saliency using the contrast against the background prototypes. Traditional saliency models mainly detect salient regions by comparing the differences between center and surround regions, which makes hard to get desired results in complex scenes for significant differences often appear both in salient and background regions in real images. Saliency detection via background prototypes contrast firstly over-segment the input image into perceptually homogeneous superpixels, and automatically identifies a series of regions far away from image center as background prototypes. The visual saliency is then accurately calculated using the color contrast with respect to the selected background prototypes. Promising experimental results demonstrate that the proposed model, which outperforms the compared state-of-the-art saliency models in average precision, recall, F-measure and visual effect, can better exclude the cluttered backgrounds, and thus produces more robust and accurate saliency maps. Moreover, due to its computational efficiency, our model is easy to be widely applied.