Salient Object Detection Based On Image Bit-Map
Bangqi Cao, Xiandong Meng, Shuyuan Zhu, Bing Zeng · 2020
In this paper, we propose a novel salient object detection framework, which makes full use of the essential image compression. More specifically, we first compose an intuitive measure of compressibility from JPEG compression, namely bit-map. Then, depending on the relationship between bitmap and salient object, we generate the salient object window directly from bit-map without utilizing any features from the compressed image. Finally, the saliency map is calculated according to the salient object window and with a ranking algorithm. The proposed method achieves good performance as well as low complexity. The experimental results demonstrate the effectiveness of our proposed method compared with other existing approaches.