Symmetry and Feature Selection in Computer Vision.

摘要

In the dissertation, two advanced computer vision techniques, named symmetry and feature selection, are proposed. The wide existence of symmetry in many image objects generates the motivation of using symmetry as a high level feature in region growing image segmentation and region-of-interest (ROI) detection in brain MRI sequences. The symmetry is explicitly applied in different forms as symmetry affinity matrix, high-level segmentation cue, statistical analysis and 3D asymmetry volume in classification features. The incorporation of symmetry provides a new effective feature to achieve the performance improvement. In the second field of my research, the feature selection with Sequential Floating Forward Selection (SFFS) as the search strategy, and with the Bayesian classifier as the evaluation metric, is applied in content-based image retrieval (CBIR), semi-supervised learning with relevance feedback, local kernel based distance metric, image classification, and online ensemble learning. It provides more compact and optimal feature sets to generate robust learning models. Experimental results on wide range of image datasets indicate the advantages of using symmetry and feature selection in computer vision tasks.