title: Unsupervised multiscale color image segmentation based on MDL principle.
abstract: 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.
authors.affiliation: Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA. qluo@fau.edu
authors.forename: Qiming
authors.initials: Q
authors.lastname: Luo
authors.forename: Taghi M
authors.initials: TM
authors.lastname: Khoshgoftaar
mesh_terms: 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*