Context aware saliency map generation using semantic segmentation
This work addresses image analysis applications like classification and recognition, but it appears incremental as it fuses existing methods (semantic, color, and contrast-based saliency maps).
The paper tackles the problem of image saliency detection by incorporating context detection through semantic segmentation as a high-level feature, achieving 99% accuracy in context detection on the Pascal-voc11 dataset and producing acceptable results for salient point detection.
Saliency map detection, as a method for detecting important regions of an image, is used in many applications such as image classification and recognition. We propose that context detection could have an essential role in image saliency detection. This requires extraction of high level features. In this paper a saliency map is proposed, based on image context detection using semantic segmentation as a high level feature. Saliency map from semantic information is fused with color and contrast based saliency maps. The final saliency map is then generated. Simulation results for Pascal-voc11 image dataset show 99% accuracy in context detection. Also final saliency map produced by our proposed method shows acceptable results in detecting salient points.