Content-Based Image Retrieval Using Relevance Feedback and Semi-Supervised Learning

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Ghasemi, Alireza | 2011

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 42019 (19)
University: Sharif University of Technology
Department: Computer Engineering
Advisor(s): Manzuri, Mohammad Taghi
Abstract:
Content-Based Image Retrieval has been an active research area in recent years, due to the vast amount of digital media available via the Internet. In this work we formulate contentbased image retrieval as machine learning problem using the relevance feedback technique and propose a learning algorithm adapted to specific properties of image retrieval. After studying specific properties of image retrieval as a machine learning problem, we propose a Bayesian framework for image retrieval based on one-class learning and test it on different image datasets. The proposed method is a kernel based approach and can also utilize domain knowledge in the form of prior knowledge in constructing a model for image data. Moreover, the proposed approach can benefit from unlabelled data samples in improvement of classification accuracy. Empirical tests show improvement compared to state-of-theart kernel-based one-class learning methods used in image retrieval such as support vector data description and one-class Gaussian process method.
Keywords:
Image Retrieval ; Machine Learning ; Pattern Recognition ; Semi-Supervised Learning ; Active Learning