Consistent image processing based on co‐saliency. Issue 3 (5th March 2021)

• Record Type: Journal Article
• Authors: Ren, Xiangnan; Li, Jinjiang; Hua, Zhen; Jiang, Xinbo
• Is Part Of: CAAI transactions on intelligence technology. Volume 6:Issue 3(2021)
• Page Start: 324
• Page End: 337
• Publication Date: 2021-03-05
• Subjects: Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/cit2.12020
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract: In a group of images, the recurrent foreground objects are considered as the key objects in the group of images. In co‐saliency detection, these are described as common saliency objects. The aim is to be able to naturally guide the user's gaze to these common salient objects. By guiding the user's gaze, users can easily find these common saliency objects without interference from other information. Therefore, a method is proposed for reducing user visual attention based on co‐saliency detection. Through the co‐saliency detection algorithm and matting algorithm for image preprocessing, the exact position of non‐common saliency objects (called Region of Interest here, i.e. ROI) in the image group can be obtained. In the attention retargeting algorithm, the internal features of the image to adjust the saliency of the ROI areas are considered. In the HSI colour space, the three components H, S, and I are adjusted separately. First, the hue distribution is constructed by the Dirac kernel function, and then the most similar hue distribution to the surrounding environment is selected as the best hue distribution of ROI areas. The S and I components can be set as the contrast difference between ROI areas and surrounding background areas according to the user's demands. Experimental results show that this method effectively reduces the ROI areas' attraction to the user's visual attention. Moreover, comparing this method with other methods, the saliency adjustment effect achieved is much better, and the processed image is more natural.