Fully automatic figure‐ground segmentation algorithm based on deep convolutional neural network and GrabCut. Issue 12 (1st December 2016)

• Record Type: Journal Article
• Authors: Fu, Ruigang; Li, Biao; Gao, Yinghui; Wang, Ping
• Is Part Of: IET image processing. Volume 10:Issue 12(2016)
• Page Start: 937
• Page End: 942
• Publication Date: 2016-12-01
• Subjects: image segmentation -- convolution -- neural nets -- feature extraction -- human computer interaction; deep convolutional neural network -- GrabCut algorithm -- image object -- human–computer interaction -- fully automatic figure‐ground segmentation algorithm -- Weizmann segmentation evaluation database; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0009
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Figure‐ground segmentation is used to extract the foreground from the background, where the foreground is usually defined as the region containing the most meaningful object of the image. In fact, the algorithms that take advantage of human–computer interaction often attain better performance and they are based on the 'one‐to‐one' model. In this study, the authors present a novel algorithm for figure‐ground segmentation based on the GrabCut algorithm, which is a common segmentation algorithm that is user interactive. However, instead of a real user, they attempt to use a pre‐trained deep convolutional neural network to interact with GrabCut for completing its job successfully. Weizmann's segmentation evaluation database is used as the test dataset and the results show that their algorithm works well for figure‐ground segmentation. While the previous automatic segmentation algorithms are required to rank their segments empirically in order to find the position of the foreground after the segmentation, their algorithm is fully automatic.