Name
Abstract | ||
|---|---|---|
In this paper, we present a novel multiscale texture model and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled, in turn, by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The "fragmentation" step allows one to find the elementary textures of the model, while the "reconstruction" step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural and remote sensing images. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TIP.2009.2020534 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
novel multiscale texture model,complex texture,hierarchical image segmentation,feature vector,texture score,unsupervised texture segmentation,hierarchical multiple markov chain,h-mmc model,real natural texture,unsupervised segmentation,prague segmentation benchmark,elementary texture,segmentation,biomedical imaging,image reconstruction,markov process,classification,pattern analysis,color image,image segmentation,markov chain model,color,markov processes,benchmark testing,image texture,markov chain,feature extraction,probability,remote sensing,image analysis | Computer vision,Feature vector,Scale-space segmentation,Markov process,Pattern recognition,Image texture,Segmentation,Markov model,Markov chain,Image segmentation,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
18 | 8 | 1057-7149 |
Citations | PageRank | References |
32 | 1.87 | 38 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giuseppe Scarpa | 1 | 204 | 23.23 |
| Raffaele Gaetano | 2 | 118 | 17.28 |
| Michal Haindl | 3 | 488 | 50.33 |
| Josiane Zerubia | 4 | 2032 | 232.91 |