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Image Segmentation using Wavelet-domain Classification (1999)
| Content Provider | CiteSeerX |
|---|---|
| Author | Choi, Hyeokho Baraniuk, Richard |
| Description | We introduce a new image texture segmentation algorithm, HMTseg, based on wavelet-domain hidden Markov tree (HMT) models. The HMT model is a tree-structured probabilistic graph that captures the statistical properties of wavelet coefficients. Since the HMT is particularly well suited to images containing singularities (edges and ridges), it provides provides a good classifier for textures. Utilizing the inherent tree structure of the wavelet HMT and its fast training and likelihood computation algorithms, we perform multiscale texture classification at various scales. We then fuse these multiscale classifications using a Bayesian probabilistic graph to obtain a reliable final segmentation. Since HMTseg works on the wavelet transform of the image, it can directly segment wavelet-compressed images, without the need for decompression. We demonstrate the performance of HMTseg with synthetic, aerial photo, and document image segmentations. 1. INTRODUCTION 1.1. Image Segmentation The image... |
| File Format | |
| Language | English |
| Publisher Date | 1999-01-01 |
| Publisher Institution | in Proceedings of SPIE technical conference on Mathematical Modeling, Bayesian Estimation, and Inverse Problems |
| Access Restriction | Open |
| Subject Keyword | Inherent Tree Structure Statistical Property Fast Training Bayesian Probabilistic Graph Document Image Segmentation Segment Wavelet-compressed Image Wavelet-domain Hidden Markov Tree Aerial Photo Multiscale Classification Image Segmentation Hmt Model New Image Texture Segmentation Algorithm Wavelet Hmt Multiscale Texture Classification Likelihood Computation Algorithm Wavelet Transform Wavelet-domain Classification Wavelet Coefficient Tree-structured Probabilistic Graph Reliable Final Segmentation Various Scale Good Classifier |
| Content Type | Text |
| Resource Type | Article |
