Fast Two-Step Histogram-Based Image Segmentation

Damir Krstinic, Ana Kuzmanic Skelin, Ivan Slapnicar
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture
University of Split

This paper is a postprint of a paper submitted to and accepted for publication in IET Image Processing and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library

Abstract:

We propose a novel image segmentation technique based on the non-parametric clustering procedure in the discretized color space. The discrete probability density function is estimated in two steps. Multidimensional color histogram is created, which is afterwards used to acquire final density estimate using the variable kernel density estimation technique. Segmentation is obtained by mapping revealed range domain clusters to the spatial image domain. The proposed method is highly efficient, running in time linear to the number of the image pixels with low constant factors. The output of the algorithm can be accommodated for a particular application to simplify the integration with other image processing techniques. Quantitative evaluation on a standard test dataset proves that the quality of the segmentations provided by the proposed method is comparable to the quality of the segmentations generated by other widely adopted low-level segmentation techniques, while running times are several times faster.

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The source code of the presented algorithm is available as tgz file: fhs.tgz
Full text in PDF format: fhs-postprint.pdf