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. Re-cently 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 out-put (including the models) may subsequently be used as a con-tent 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. 1