Novel feature representation and matching techniques for content-based image retrieval
This thesis presents novel feature representation schemes and matching techniques for content-based image retrieval. There are three main contributions reported in the thesis. They include: (1) a block-constrained fractal coding scheme and an improved nona-tree decomposition based matching technique for image retrieval; (2) a thinning-based starting point localization method and the application of a fuzzy integral to the combination of different shape feature sets for plant leaf image retrieval; and (3) tree-structured content representation and its adaptive processing for content-based image retrieval with relevance feedback. 
In the first investigation, an image is partitioned into non-overlap blocks of a size similar to that of an iconic query image. Fractal code is efficiently generated for each block individually. For the similarity measure in matching the fractal code of two images, an improved nona-tree decomposition scheme is adopted to avoid matching the fractal code globally so as to significantly reduce computational complexity. 
In the second investigation, a robust fuzzy integral method is used to combine three shape feature sets, namely, centroid-contour distance (CCD) curve, moment invariants (MIs), and angle code histogram (ACH), for shape-based plant leaf image retrieval. Different from MIs and the ACH feature set, the CCD curve is neither scale nor rotation invariant. Hence, a normalization scheme is needed for the CCD curve to achieve scale invariance, and an efficient starting point localization method is required to achieve the rotation invariance with the similarity measure of CCD curves. In this investigation, a thinning-based method is proposed to locate possible starting points of a leaf contour to make our approach more computationally efficient for image matching. Our proposed starting point localization method can also benefit other shape representation schemes that are sensitive to starting points. In order to combine the three feature sets objectively yet consistently with human perception, a fuzzy integral is employed to combine the similarity measures of the three shape feature sets. The fuzzy integral approach has two distinct advantages: (1) releasing the user from a burden of tuning the combination parameters required for a weighted summation based approach; (2) guaranteeing an optimal or near optimal combination performance. 
In the third investigation, a novel structural representation of image content and shape pattern, and its adaptive processing is proposed for content-based image retrieval with relevance feedback. (Abstract shortened by UMI.)