MSDNN: Multi-Scale Deep Neural Network for Salient Object Detection

TL;DR

This paper introduces MSDNN, a multi-scale deep neural network that effectively combines global features and multi-scale representations to improve salient object detection performance.

Contribution

The paper proposes a novel multi-scale deep neural network architecture with a fusion module, advancing the state-of-the-art in salient object detection.

Findings

Outperforms 12 existing methods on benchmark datasets

Effectively combines global context with multi-scale features

Achieves significant improvements in saliency map accuracy

Abstract

Salient object detection is a fundamental problem and has been received a great deal of attentions in computer vision. Recently deep learning model became a powerful tool for image feature extraction. In this paper, we propose a multi-scale deep neural network (MSDNN) for salient object detection. The proposed model first extracts global high-level features and context information over the whole source image with recurrent convolutional neural network (RCNN). Then several stacked deconvolutional layers are adopted to get the multi-scale feature representation and obtain a series of saliency maps. Finally, we investigate a fusion convolution module (FCM) to build a final pixel level saliency map. The proposed model is extensively evaluated on four salient object detection benchmark datasets. Results show that our deep model significantly outperforms other 12 state-of-the-art approaches.