Open AccessFully automatic figure‐ground segmentation algorithm based on deep convolutional neural network and GrabCut
Author(s) -
Fu Ruigang,
Li Biao,
Gao Yinghui,
Wang Ping
Publication year - 2016
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2016.0009
Subject(s) - segmentation , computer science , artificial intelligence , convolutional neural network , ground truth , pattern recognition (psychology) , image segmentation , segmentation based object categorization , scale space segmentation , computer vision , algorithm
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.