Color segmentation using MDL clustering

Richard S. Wallace; Yasuhito Suenaga

doi:10.1117/12.25174

1 February 1991 Color segmentation using MDL clustering

Richard S. Wallace, Yasuhito Suenaga

Proceedings Volume 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques; (1991) https://doi.org/10.1117/12.25174
Event: Advances in Intelligent Robotics Systems, 1990, Boston, MA, United States

Abstract

This paper describes a procedure for segmentation of color face images. A cluster analysis algorithm uses a subsample of the input image color pixels to detect clusters in color space. The clustering program consists of two parts. The first part searches for a hierarchical clustering using the NIHC algorithm. The second part searches the resultant cluster tree for a level clustering having minimum description length (MDL). One of the primary advantages of the MDL paradigm is that it enables writing robust vision algorithms that do not depend on user-specified threshold parameters or other " magic numbers. " This technical note describes an application of minimal length encoding in the analysis of digitized human face images at the NTT Human Interface Laboratories. We use MDL clustering to segment color images of human faces. For color segmentation we search for clusters in color space. Using only a subsample of points from the original face image our clustering program detects color clusters corresponding to the hair skin and background regions in the image. Then a maximum likelyhood classifier assigns the remaining pixels to each class. The clustering program tends to group small facial features such as the nostrils mouth and eyes together but they can be separated from the larger classes through connected components analysis.

Citation Download Citation

Richard S. Wallace and Yasuhito Suenaga "Color segmentation using MDL clustering", Proc. SPIE 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques, (1 February 1991); https://doi.org/10.1117/12.25174

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