Non-Local Deep Features for Salient Object Detection

Zhiming Luo, Akshaya Mishra, Andrew Achkar, Justin Eichel, Shaozi Li, Pierre-Marc Jodoin; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 6609-6617


Saliency detection aims to highlight the most relevant objects in an image. Methods using conventional models struggle whenever salient objects are pictured on top of a cluttered background while deep neural nets suffer from excess complexity and slow evaluation speeds. In this paper, we propose a simplified convolutional neural network which combines local and global information through a multi-resolution 4x5 grid structure. Instead of enforcing spacial coherence with a CRF or superpixels as is usually the case, we implemented a loss function inspired by the Mumford-Shah functional which penalizes errors on the boundary. We trained our model on the MSRA-B dataset, and tested it on six different saliency benchmark datasets. Results show that our method is on par with the state-of-the-art while reducing computation time by a factor of 18 to 100 times, enabling near real-time, high performance saliency detection.

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author = {Luo, Zhiming and Mishra, Akshaya and Achkar, Andrew and Eichel, Justin and Li, Shaozi and Jodoin, Pierre-Marc},
title = {Non-Local Deep Features for Salient Object Detection},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}