Unsupervised multiscale color image segmentation based on MDL principle.
We present an unsupervised multiscale color image segmentation algorithm. The basic idea is to apply mean shift clustering to obtain an over-segmentation and then merge regions at multiple scales to minimize the minimum description length criterion. The performance on the Berkeley segmentation benchmark campares favorably with some existing approaches.
Luo
Qiming
Q
Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA. qluo@fau.edu
Khoshgoftaar
Taghi M
TM
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003116:Color; D003124:Colorimetry / Q000379:methods*; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*