An Effective Segmentation Pattern Using Multi-class Independent Component Analysis on High Quality Color Texture Images

N Balakrishnan,S.P Shantharajah

doi:10.19026/rjaset.12.2809

Abstract

An efficient segmentation pattern proposes to improve the efficiency of segmentation through Multi-Class Independent Component InfoMax Analysis (MICIA) on multi-class high-quality color images. To attain richer segmentation of color, textures with minimal computation time, MICIA combines the watershed cuts principle and Minimal Spanning Forest method. The higher quality texture image is segmented by using the watershed cuts principle. Watershed cuts principle in MICIA is associated with regional minima of the map to handle multi-class poorly defined boundary images. Independent Component Analysis (ICA) is based on InfoMax which achieves richer segmentation of color textures with maximum likelihood function. ICA is based on InfoMax. It handles multi-class texture images. Because of the ICA maximum likelihood ensures higher independence on segmentation cuts.This produces an effective segmentation which can be used to improve the appearance of the high-quality images. To prove the efficiency, the experiment is conducted on factors such as sub-pixel accuracy rate on segmenting, multi-class image segmentation time and true positive rate.

Highlights

The effective segmentation pattern is still a challenging task, though many segmentation methods and approaches have been presented in the recent years
Effective segmentation of multi-class high quality color images has become the key for image processing
To achieve richer segmentation of color, texture with minimal computation time and improve the level of Multi-Class Independent Component InfoMax Analysis (MICIA) is associated with regional minima of the map to handle multi-class poorly defined boundary images improving the multi-class image segmentation time by 62-89% when compared to Unsupervised Image Segmentation (UIS) (McCann et al, 2014)

Summary

INTRODUCTION

The effective segmentation pattern is still a challenging task, though many segmentation methods and approaches have been presented in the recent years. A combinatorial framework (Thakoor et al, 2010) has been designed to keep in mind the optimization of the cost function It combines three factors, namely, maximum likelihood of hypotheses, cost involved in clustering and distribution of an outlier in a uniform manner to minimize computational complexity. An improved threshold-based segmentation (Abdullaha et al, 2012) has been presented to the partition of the natural images in a clear manner and reduces the complexity involvement during computation applying an inverse technique. As a result of ICA based on InfoMax handling multi-class texture images, the Maximum likelihood ensures higher independence on segmentation cuts. High-quality texture image reduces the computation time on multi-class images and improves the sub-pixel accuracy rate on segmenting

LITERATURE REVIEW

METHODS AND MATERIALS

Findings

CONCLUSION