Contextual information based visual saliency model
Seungchul Ryu, Bumsub Ham, Kwanghoon Sohn · 2013
Automatic detection of visual saliency has been considered a very important task because of a wide range of applications such as object detection, image quality assessment, image segmentation, and more. Thanks to active researches in this field, many effective saliency models have been developed. Nevertheless, several challenging problems are still remain unsolved, such as detecting saliency in complex scene and providing high resolution and accurate saliency maps. In order to address such challenging problems, we propose a visual saliency model based on the concept of contextual information. First, we introduce a general framework for detecting saliency of an image using contextual information. Then, the proposed saliency model based on color and shape features is proposed. Quantitative and qualitative comparisons with seven state-of-the-art models on the public database show that the proposed model achieves excellent performance. Especially, the proposed model can provide good performance on challenging images including images with cluttered background and repeating distractors compared to the other models.