Importance of Image Enhancement Techniques in Color Image Segmentation: A Comprehensive and Comparative Study

TL;DR

This paper evaluates various image enhancement techniques to improve color image segmentation accuracy, analyzing their effectiveness across HSV and LAB color spaces to identify the most suitable combination.

Contribution

It provides a comprehensive comparison of enhancement techniques and their impact on segmentation performance in different color spaces, filling a gap in existing research.

Findings

Certain enhancement techniques significantly improve segmentation accuracy.

HSV color space generally outperforms LAB for enhanced segmentation.

Optimal enhancement methods depend on the chosen color space.

Abstract

Color image segmentation is a very emerging research topic in the area of color image analysis and pattern recognition. Many state-of-the-art algorithms have been developed for this purpose. But, often the segmentation results of these algorithms seem to be suffering from miss-classifications and over-segmentation. The reasons behind these are the degradation of image quality during the acquisition, transmission and color space conversion. So, here arises the need of an efficient image enhancement technique which can remove the redundant pixels or noises from the color image before proceeding for final segmentation. In this paper, an effort has been made to study and analyze different image enhancement techniques and thereby finding out the better one for color image segmentation. Also, this comparative study is done on two well-known color spaces HSV and LAB separately to find out which color space supports segmentation task more efficiently with respect to those enhancement techniques.