Context aware saliency map generation using semantic segmentation

TL;DR

This paper introduces a novel saliency map generation method that leverages semantic segmentation for context detection, combining high-level semantic features with traditional saliency cues to improve accuracy.

Contribution

It presents a new approach that integrates semantic segmentation into saliency detection, enhancing the detection of salient regions by incorporating high-level contextual information.

Findings

Achieved 99% accuracy in context detection on Pascal-voc11 dataset.

Produced saliency maps with acceptable accuracy in identifying salient points.

Demonstrated the effectiveness of semantic information fusion in saliency detection.

Abstract

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.