A multilayer backpropagation saliency detection algorithm and its applications
This work addresses saliency detection for multimedia applications, particularly in complex scenes with multiple objects or backgrounds, but it appears incremental as it builds on existing depth-based methods.
The paper tackles the problem of saliency detection in complex scenes by proposing a multilayer backpropagation algorithm that uses depth cues from three image layers, resulting in superior performance compared to existing methods. It also demonstrates applications in scene reconstruction and small target detection.
Saliency detection is an active topic in the multimedia field. Most previous works on saliency detection focus on 2D images. However, these methods are not robust against complex scenes which contain multiple objects or complex backgrounds. Recently, depth information supplies a powerful cue for saliency detection. In this paper, we propose a multilayer backpropagation saliency detection algorithm based on depth mining by which we exploit depth cue from three different layers of images. The proposed algorithm shows a good performance and maintains the robustness in complex situations. Experiments' results show that the proposed framework is superior to other existing saliency approaches. Besides, we give two innovative applications by this algorithm, such as scene reconstruction from multiple images and small target object detection in video.