Department   Undergraduate School  , School of Science and Technology
   Position   Associate Professor
Language English
Publication Date 2007/03
Type Academic Journal
Peer Review Peer reviewed
Title Image Segmentation Using MAP-MRF Estimation and Support Vector Machine
Contribution Type Co-authored (first author)
Journal Interdisciplinary Information Sciences
Journal TypeAnother Country
Publisher Tohoku University
Volume, Issue, Page 13(1),pp.33-42
Author and coauthor Tadaaki Hosaka,Takumi Kobayashi,Nobuyuki Otsu
Details Image segmentation has recently been studied in a framework of maximum a posteriori estimation for the Markov random field, where the cost function representing pixel-wise likelihood and inter-pixel smoothness should be minimized. The common drawback of these studies is the decrease in performance when a foreground object and the background have similar colors. We propose the likelihood formulation in the cost function considering not only a single pixel but also its neighboring pixels, and utilizing the support vector machine to enhance the discrimination between foreground and background. The global optimal solution for our cost function can be realized by the graph cut algorithm. Experimental results show an excellent segmentation performance in many cases.
DOI 10.4036/iis.2007.33
ISSN 1340-9050
NAID 110006274935
PermalinkURL http://hdl.handle.net/10097/17445
URL for researchmap https://jlc.jst.go.jp/DN/JALC/00291771207?from=CiNii