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Appl. Sci. 2018, 8(4), 500;

A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC

Key Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi’an 710071, China
School of Aerospace Science and Technology, Xidian University, Xi’an 710071, China
Author to whom correspondence should be addressed.
Received: 12 February 2018 / Revised: 17 March 2018 / Accepted: 23 March 2018 / Published: 26 March 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
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An image semantic segmentation algorithm using fully convolutional network (FCN) integrated with the recently proposed simple linear iterative clustering (SLIC) that is based on boundary term (BSLIC) is developed. To improve the segmentation accuracy, the developed algorithm combines the FCN semantic segmentation results with the superpixel information acquired by BSLIC. During the combination process, the superpixel semantic annotation is newly introduced and realized by the four criteria. The four criteria are used to annotate a superpixel region, according to FCN semantic segmentation result. The developed algorithm can not only accurately identify the semantic information of the target in the image, but also achieve a high accuracy in the positioning of small edges. The effectiveness of our algorithm is evaluated on the dataset PASCAL VOC 2012. Experimental results show that the developed algorithm improved the target segmentation accuracy in comparison with the traditional FCN model. With the BSLIC superpixel information that is involved, the proposed algorithm can get 3.86%, 1.41%, and 1.28% improvement in pixel accuracy (PA) over FCN-32s, FCN-16s, and FCN-8s, respectively. View Full-Text
Keywords: semantic segmentation; FCN; BSLIC; superpixel semantic segmentation; FCN; BSLIC; superpixel

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Zhao, W.; Zhang, H.; Yan, Y.; Fu, Y.; Wang, H. A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC. Appl. Sci. 2018, 8, 500.

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