TY - GEN
T1 - A subjective method for image segmentation evaluation
AU - Wang, Qi
AU - Wang, Zengfu
PY - 2010
Y1 - 2010
N2 - Image segmentation is an important processing step in many image understanding algorithms and practical vision systems. Various image segmentation algorithms have been proposed and most of them claim their superiority over others. But in fact, no general acceptance has been gained of the goodness of these algorithms. In this paper, we present a subjective method to assess the quality of image segmentation algorithms. Our method involves the collection of a set of images belonging to different categories, optimizing the input parameters for each algorithm, conducting visual evaluation experiments and analyzing the final results. We outline the framework through an evaluation of four state-of-the-art image segmentation algorithms - mean-shift segmentation, JSEG, efficient graph based segmentation and statistical region merging, and give a detailed comparison of their different aspects.
AB - Image segmentation is an important processing step in many image understanding algorithms and practical vision systems. Various image segmentation algorithms have been proposed and most of them claim their superiority over others. But in fact, no general acceptance has been gained of the goodness of these algorithms. In this paper, we present a subjective method to assess the quality of image segmentation algorithms. Our method involves the collection of a set of images belonging to different categories, optimizing the input parameters for each algorithm, conducting visual evaluation experiments and analyzing the final results. We outline the framework through an evaluation of four state-of-the-art image segmentation algorithms - mean-shift segmentation, JSEG, efficient graph based segmentation and statistical region merging, and give a detailed comparison of their different aspects.
KW - Image segmentation
KW - Subjective evaluation
UR - http://www.scopus.com/inward/record.url?scp=78650490195&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-12297-2_6
DO - 10.1007/978-3-642-12297-2_6
M3 - 会议稿件
AN - SCOPUS:78650490195
SN - 3642122965
SN - 9783642122965
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 53
EP - 64
BT - Computer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers
T2 - 9th Asian Conference on Computer Vision, ACCV 2009
Y2 - 23 September 2009 through 27 September 2009
ER -