Salient object detection via a novel saliency ranking tree integrating low-level and high-level features
Yu Pang, Yang Huang, Xiaosheng Yu, Chengdong Wu · 2024
In this paper, we propose a novel salient object detection framework by constructing a novel saliency tree model integrating low-level and high-level features. In our model, numerous features containing low-level features (e.g., color, texture, gradient, contrast, etc.) and high-level features (e.g., deep features extracted from pre-trained VGG19 net) are firstly selected as candidate features. We develop a novel feature integrating mechanism to acquire an integrated feature descriptor which is more discriminative to capture the contrast between foreground and background for the input image. Then, we construct a novel saliency tree model relied on the integrated features to generate saliency map. We compare the proposed method and other state-of-the-art methods on three datasets, experimental results indicate that the proposed saliency detection algorithm has achieved the top performance.