Multiple salient object detection through multi-level foreground segmentation strategy

This article presents a novel method for identifying multiple salient regions using a combination of foreground clues. While previous research has been successful in detecting salient objects, detecting multiple objects presents unique challenges due to high dissimilarity between objects. The authors have introduced two frameworks, namely non-parametric cluster-based saliency and parametric cluster-based saliency, in order to tackle a problem. The problem pertains to the precise localization and segmentation of salient objects that have clear boundaries and uniform interiors. To address this issue, the authors have developed a deep neural network that directs attention and boundaries towards salient objects. The network employs a channel-wise attention module that assigns greater weight to the significant feature channels. The proposed work has been experimented on various standard datasets and has outperformed existing methods. The paper presents an innovative approach for detecting multiple salient regions, which overcomes the limitations of existing methods. By using foreground clues and a multi-level foreground segmentation strategy, the proposed approach can accurately identify multiple salient regions, regardless of their dissimilarity, size, or shape. In order to address the drawbacks of current approaches, a thorough and innovative method for salient object recognition is presented in this study. By using a combination of frameworks and a deep neural network, the suggested approach can precisely identify multiple salient regions.