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2002
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Unsupervised image segmentation via Markov trees and complex wavelets

9 years 9 months ago
Unsupervised image segmentation via Markov trees and complex wavelets
The goal in image segmentation is to label pixels in an image based on the properties of each pixel and its surrounding region. Recently Content-Based Image Retrieval (CBIR) has emerged as an application area in which retrieval is attempted by trying to gain unsupervised access to the image semantics directly rather than via manual annotation. To this end, we present an unsupervised segmentation technique in which colour and texture models are learned from the image prior to segmentation, and whose output (including the models) may subsequently be used as a content descriptor in a CBIR system. These models are obtained in a multiresolution setting in which Hidden Markov Trees (HMT) are used to model the key statistical properties exhibited by complex wavelet and scaling function coefficients. The unsupervised Mean Shift Iteration (MSI) procedure is used to determine a number of image regions which are then used to train the models for each segmentation class.
Cián W. Shaffrey, Ian Jermyn, Nick G. Kings
Added 24 Oct 2009
Updated 24 Oct 2009
Type Conference
Year 2002
Where ICIP
Authors Cián W. Shaffrey, Ian Jermyn, Nick G. Kingsbury
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