Abstract
Automatic classification and recognition of images and video is one of the challenging tasks of digital image processing. This paper presents performance analysis of K-means and fuzzy K-means clustering algorithms along with experimental study. The objectives of this paper are automatic extraction of features from images of vehicles and pedestrians and classify them. A good set of features that capture the most important properties of an object are used to identify the objects uniquely. The objects are classified into three different clusters such as pedestrians, light vehicles and heavy vehicles. The experimental studies were performed in MATLAB for K-means and c-means clustering algorithms. K-means clustering proven to be more effective than fuzzy c-means clustering algorithm.
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