Paper Info
Title
A Hierarchical Finite-State Model for Texture Segmentation
Abstract
A novel model for unsupervised segmentation of texture images is presented. The image to be segmented is first discretized and then a hierarchical finite-state region-based model is automatically coupled with the data by means of a sequential optimization scheme, namely the texture fragmentation and reconstruction (TFR) algorithm. Both intra- and inter-texture interactions are modeled, by means of an underlying hierarchical finite-state model, and eventually the segmentation task is addressed in a completely unsupervised manner. The output is then a nested segmentation, so that the user may decide the scale at which the segmentation has to be provided. TFR is composed of two steps: the former focuses on the estimation of the states at the finest level of the hierarchy, and is associated with an image fragmentation, or over-segmentation; the latter deals with the reconstruction of the hierarchy representing the textural interaction at different scales.
Year
DOI
Venue
2007
10.1109/ICASSP.2007.366131
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference
Keywords
Field
DocType
image reconstruction,image segmentation,image texture,optimisation,hierarchical finite-state region-based model,image fragmentation,image texture segmentation,sequential optimization scheme,texture fragmentation-reconstruction algorithm,unsupervised segmentation,Markov chain,Segmentation,classification,co-occurrence matrix,structural models,texture synthesis
Iterative reconstruction,Scale-space segmentation,Pattern recognition,Co-occurrence matrix,Segmentation,Image texture,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Texture synthesis
Conference
Volume
ISSN
ISBN
1
1520-6149
1-4244-0727-3
Citations 
PageRank 
References 
14
0.90
5
Authors
3
Name
Order
Citations
PageRank
Giuseppe Scarpa120423.23
Michal Haindl248850.33
Josiane Zerubia32032232.91