Perceptual Dimensions for Surface Texture Retrieval

This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognise that the copyright rests with its author and that no quotation from the thesis and no information derived from it may be published without the prior written consent of the author or of the University (as may be appropriate). ii This thesis presents a methodology for developing perceptually relevant surface texture retrieval systems. Generally such systems have been researched using image texture which has been captured under unknown or uncontrolled conditions (e.g. Brodatz). However, it is known that changes in illumination affect both the visual appearance of surfaces and the computational features extracted from their images. In contrast this thesis either uses surface information directly, or computes features obtained from images captured under controlled lighting conditions. Psychophysical experiments were conducted in which observers were asked to place texture samples into groups. Multidimensional Scaling was applied to the resulting similarity matrices to obtain a more manageable reduced perceptual space. A four-dimensional representation was found to capture the majority of the variability. A corresponding feature space was created by linearly combining selected trace transform features. Retrieval was performed simply by determining the n closest neighbours to the query’s feature vector. An average retrieval precision of 60 % was obtained in blind tests