Hierarchical Defect Detection Based On Reinforcement Learning

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.