Deep learning framework for saliency object detection based on global prior and local context

Lihua Fu; Haogang Ding; Cancan Li; Dan Wang; Yujia Feng

doi:10.1117/1.JEI.27.5.053003

10 September 2018 Deep learning framework for saliency object detection based on global prior and local context

Lihua Fu, Haogang Ding, Cancan Li, Dan Wang, Yujia Feng

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 5, 053003 (September 2018). https://doi.org/10.1117/1.JEI.27.5.053003

Abstract

The saliency object detection is a hot topic of computer vision. Traditional saliency detection methods are overly dependent on handcrafted low-level features. The saliency detection methods based on deep learning can effectively solve the problem, which extracts high-level features automatically. However, there are some noises in the extracted high-level features that affect the detection performance. We propose a deep learning framework for saliency detection based on global prior and local context. First, we use feature maps generated by combining some middle-level features as the input of global-prior-based deep learning model, which can reduce the interference of distracting feature information for the saliency detection. Then, two deep learning models use respectively local contexts of color image and depth map as input, which combine global prior to generate the initial saliency map. Finally, the optimized saliency map can be obtained based on spatial consistence and appearance similarity. Experiments on two publicly available datasets show that the proposed method performs better than other nine state-of-the-art approaches.

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Lihua Fu, Haogang Ding, Cancan Li, Dan Wang, and Yujia Feng "Deep learning framework for saliency object detection based on global prior and local context," Journal of Electronic Imaging 27(5), 053003 (10 September 2018). https://doi.org/10.1117/1.JEI.27.5.053003

Received: 5 March 2018; Accepted: 15 August 2018; Published: 10 September 2018

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KEYWORDS

RGB color model

Image segmentation

Convolution

Visualization

Feature extraction

Convolutional neural networks

Lithium

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