Publication details

A Hierarchical Finite-State Model for Texture Segmentation

Conference Paper (international conference)

Scarpa G., Haindl Michal, Zerubia J.


serial: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'07) /32./, p. 1209-1212

action: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'07) /32./, (Honolulu, US, 15.04.2007-20.04.2007)

research: CEZ:AV0Z10750506

project(s): 1ET400750407, GA AV ČR, 507752,

keywords: image segmentation, texture, Markov random fields

abstract (eng):

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.

abstract (cze):

Nový model neřízené segmentace texturních obrazů je studován v článku. Segmentovaný obraz se nejprve diskretizuje a potom hierarchický model s konečnými stavy je automaticky naučen na datech pomocí sekvenčního optimalizačního algoritmu nazvaného Texture Fragmentation and Reconstruction (TFR) algoritmus. Jak intra, tak inter texturní interakce jsou modelovány pomocí tohoto hierarchického modelu s konečnými stavy a segmentace je uskutečněna zcela neřízeným způsobem. Výsledkem je hierarchická segmentace, kde se uživatel může rozhodnout pro její měřítko. TFR se skládá ze dvou kroků, odhadu stavů a obrazové fragmentace.

RIV: BD