Fuzzy Rule Based Color Image Extraction

Abstract

Fuzzy rule base design for image segmentation and subsequent extraction becomes a popular one in the field of image processing. It is important to find visual attention regions with the help of low cost solutions. The aim of image segmentation is the domain-independent partition of the image into a set of regions, which are visually distinct and uniform with respect to some property, such as grey level, texture or colour. The color fundamental process followed by the human brain in perceiving color is a physio-psychological phenomenon that is not yet fully understood, the physical nature of color can be expressed on a formal basis supported by experimental and theoretical results. Basically, the colors we perceive in an object are determined by the nature of the light reflected from the object. Due to the structure of human eye, all colors are seen as variable combinations of the three so-called Primary colors Red, Green and Blue (RGB). Most of the times, acquiring spurious free preprocessing data require a lot of application cum mathematical intensive background works. We propose a feature based fuzzy rule guided novel technique that is functionally devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, we take recourse to effective metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE) and Peak Signal to Noise Ratio (PSNR).