Probabilistic rough entropy measures in image segmentation

Abstract

In numerous data clustering problems, the main priority remains a constant demand on development of new improved algorithmic schemes capable of robust and correct data handling. This requirement has been recently boosted by emerging new technologies in data acquisition area. In image processing and image analysis procedures, the image segmentation procedures have the most important impact on the image analysis results. In data analysis methods, in order to improve understanding and description of data structures, many innovative approaches have been introduced. Data analysis methods always strongly depend upon revealing inherent data structure. In the paper, a new algorithmic Rough Entropy Framework - (REF, in short) has been applied in the probabilistic setting. Crisp and Fuzzy RECA measures (Rough Entropy Clustering Algorithm) introduced in [5] are extended into probability area. The basic rough entropy notions, the procedure of rough (entropy) measure calculations and examples of probabilistic approximations have been presented and supported by comparison to crisp and fuzzy rough entropy measures. In this way, uncertainty measures have been combined with probabilistic procedures in order to obtain better insight into data internal structure. © 2010 Springer-Verlag Berlin Heidelberg.