# Feature-Based Image Clustering and Segmentation Using Wavelets

## TL;DR

This paper introduces a novel approach that integrates Wavelet features into traditional clustering and segmentation algorithms, enhancing robustness and allowing frequency-based segmentation control.

## Contribution

It proposes a new method to incorporate Wavelet features into K-means, FCM, and ACWE algorithms, with a weighting parameter for frequency information, improving segmentation robustness.

## Key findings

- Enhanced segmentation robustness with Wavelet features
- Ability to control segmentation results via frequency weighting
- Algorithms converge to different results based on Wavelet sub-bands

## Abstract

Pixel intensity is a widely used feature for clustering and segmentation algorithms, the resulting segmentation using only intensity values might suffer from noises and lack of spatial context information. Wavelet transform is often used for image denoising and classification. We proposed a novel method to incorporate Wavelet features in segmentation and clustering algorithms. The conventional K-means, Fuzzy c-means (FCM), and Active contour without edges (ACWE) algorithms were modified to adapt Wavelet features, leading to robust clustering/segmentation algorithms. A weighting parameter to control the weight of low-frequency sub-band information was also introduced. The new algorithms showed the capability to converge to different segmentation results based on the frequency information derived from the Wavelet sub-bands.