Exploiting the Value of the Center-dark Channel Prior for Salient Object Detection
This work addresses the problem of detecting attractive objects in images for applications like computer vision, but it is incremental as it builds on existing priors and methods.
The paper tackles salient object detection in RGB-D images by proposing an algorithm that fuses an initial saliency map with a center-dark channel map, achieving favorable performance against state-of-the-art methods on four benchmark datasets.
Saliency detection aims to detect the most attractive objects in images and is widely used as a foundation for various applications. In this paper, we propose a novel salient object detection algorithm for RGB-D images using center-dark channel priors. First, we generate an initial saliency map based on a color saliency map and a depth saliency map of a given RGB-D image. Then, we generate a center-dark channel map based on center saliency and dark channel priors. Finally, we fuse the initial saliency map with the center dark channel map to generate the final saliency map. Extensive evaluations over four benchmark datasets demonstrate that our proposed method performs favorably against most of the state-of-the-art approaches. Besides, we further discuss the application of the proposed algorithm in small target detection and demonstrate the universal value of center-dark channel priors in the field of object detection.