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Feature-Based Image Clustering and Segmentation Using Wavelets

2019-07-05 02:30:07
Junyu Chen, Eric C. Frey
arXiv_AI
           
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.

Abstract (translated)

URL

https://arxiv.org/abs/1907.03591

PDF

https://arxiv.org/pdf/1907.03591.pdf

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