Unsupervised Segmentation of Natural Images Based on the Adaptive Integration of Colour-Texture Descriptors

This thesis presents the development of a theoretical framework capable of encompassing the colour and texture information in a robust image descriptor that can be applied to the identification of coherent regions in complex natural images. In the suggested approach, the colour and texture features are extracted explicitly on two independent channels and the main emphasis of this work was placed on their adaptive inclusion in the process of data partition. The proposed segmentation framework consists of several computational tasks including adaptive filtering, colour segmentation, texture extraction and the adaptive integration of the colour and texture features in the segmentation process in an unsupervised manner. In this regard, an important contribution of this work is represented by the development of a multi-space colour segmentation scheme where the key element was the inclusion of the Self Organising Map network that was applied to compute the optimal parameters required by data clustering algorithms. The second component of the proposed segmentation framework deals with the extraction of texture features and in this study the performance of several texture descriptors when applied to the segmentation of synthetic and natural images was analysed. To this end, several texture descriptors are evaluated including multi-channel texture decomposition filtering based on Gabor and isotropic filter banks, and multiresolution approaches that analyse the texture at micro-level. The most important contribution of this work resides in the adaptive inclusion of the colour and texture features in a compound mathematical descriptor with the aim of identifying the homogenous regions in natural images. This colourtexture integration is performed by a novel clustering algorithm that is able to enforce the spatial continuity during the data assignment process. To demonstrate the efficiency of the proposed colour-texture segmentation scheme, a comprehensive quantitative and qualitative performance evaluation has been carried out on natural image databases. The experimental results indicate that the proposed framework is accurate in capturing the colour and texture characteristics even when applied to the segmentation of complex natural images.