Lee, Minhyeok Park, Chaewon Cho, Suhwan Lee, Sangyoun 2024-01-12T02:49:34Z 2024-01-12T02:49:34Z 2023-10-29 2022-10 1522-4880 https://pubs.kist.re.kr/handle/201004/76605 Co-saliency detection is a task to segment the occurring salient objects in a group of images. The biggest challenges are distracting objects in the background and ambiguity between the foreground and background. To handle these issues, we propose a novel superpixel group-correlation network (SGCN) architecture that uses a superpixel algorithm to obtain various component features from a group of images and creates a group-correlation matrix to detect the common components of those images. In this way, non-common objects can be effectively excluded from consideration, enabling a clear distinction between foreground and background. Our method outperforms current state-of-the-art methods on three popular benchmark datasets for co-saliency detection, and our extensive experiments thoroughly validate our claimed contributions. English IEEE Superpixel Group-Correlation Network for Co-Saliency Detection Conference 10.1109/ICIP46576.2022.9897408 1 IEEE International Conference on Image Processing (ICIP), pp.806 - 810 IEEE International Conference on Image Processing (ICIP) 806 810 US Bordeaux, FRANCE 2022-10-16 2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP 001058109500157 2-s2.0-85146705041