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.
|Title of host publication||2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|Publication status||Published - 2022|
|Event||29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, France|
Duration: 2022 Oct 16 → 2022 Oct 19
|Name||Proceedings - International Conference on Image Processing, ICIP|
|Conference||29th IEEE International Conference on Image Processing, ICIP 2022|
|Period||22/10/16 → 22/10/19|
Bibliographical noteFunding Information:
Acknowledgement. This research was supported by R&D program for Advanced Integrated-intelligence for Identification (AIID) through the National Research Foundation of KOREA(NRF) funded by Ministry of Science and ICT (NRF-2018M3E3A1057289) and the KIST Institutional Program(Project No.2E31051-21-203).
© 2022 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Signal Processing