Paper Info
Title
Unsupervised color-texture segmentation based on multiscale quaternion Gabor filters and splitting strategy
Abstract
This paper proposes a new method for color-texture segmentation based on a splitting framework with graph cut technique. To process the scale difference of quaternion Gabor filter (QGF) features of a color textured image, a new multiscale QGF (MQGF) is introduced to describe texture attributes of the given image. Then, the segmentation is formulated in terms of energy minimization gradually obtained using binary graph cuts, where color and MQGF features are modeled with a multivariate finite mixture model, and minimum description length (MDL) principle is integrated into this framework as a splitting criterion. In contrast to previous approaches, our method finds an optimal segmentation by balancing energy cost and coding length, and the segmentation result is determined during the splitting process automatically. Experimental results on both synthetic and real natural color textured images demonstrate the good performance of the proposed method.
Year
DOI
Venue
2013
10.1016/j.sigpro.2013.02.010
Signal Processing
Keywords
Field
DocType
gabor filter,unsupervised color-texture segmentation,color textured image,splitting process,multiscale quaternion,color-texture segmentation,splitting strategy,segmentation result,new method,splitting criterion,splitting framework,optimal segmentation,real natural color,minimum description length,graph cuts
Cut,Mathematical optimization,Scale-space segmentation,Pattern recognition,Segmentation,Minimum description length,Segmentation-based object categorization,Image segmentation,Gabor filter,Artificial intelligence,Mixture model,Mathematics
Journal
Volume
Issue
ISSN
93
9
0165-1684
Citations 
PageRank 
References 
8
0.44
29
Authors
4
Name
Order
Citations
PageRank
Lei Li180.44
Lianghai Jin218515.07
Xiangyang Xu37610.40
Enmin Song417624.53