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Statistical Learning Techniques in Image Retrieval

Author: HeJingZuo
Tutor: ZhaoNanYuan
School: Tsinghua University
Course: Control Science and Engineering
Keywords: Image Retrieval Relevance feedback Active Learning More random walk model Support Vector Machine
CLC: TP391.3
Type: Master's thesis
Year: 2005
Downloads: 267
Quote: 2
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Abstract


With the development of network and computer technology , image retrieval technology in people's working lives are increasingly important role stand out . However, due to the existence of low-level features and high-level semantic inconsistencies between , as well as in the retrieval process learning problems faced by small sample size , limiting the wide application of this technology . On the other hand , statistical learning methods for people to understand an important tool for the objective world , it is in image retrieval application has not been fully explored. This paper focuses on the application of statistical learning methods to solve traditional problems in image retrieval . Our work can be divided into the following three parts: First , in order to solve image retrieval small sample learning problem , this paper proposes a multi- called random walk model (MRW) of transductive learning framework . In theory , MRW unify some of the existing semi-supervised learning algorithm ; in the application , MRW dramatically improves the image retrieval performance. Secondly , to address the relevance feedback image selection problem , this paper proposes a named MeanVersion Space (MVS) active learning methods . In theory , MVS integrated image retrieval some of the existing active learning method ; in the application , MVS accelerated the search concept of learning, so as to enhance the performance of the image retrieval . Finally, in order to solve the pseudo- relevance feedback retrieval problem , this paper proposes a probability outputs a named iterator class support vector machine (IPOCS) learning method ; theoretically , IPOCS the existing multi-view algorithm from the semi- supervised extended to the case of unsupervised situation. In applications , IPOCS full use of the Web search engine original sorting results , thus greatly improving the search results. In theory, this work on statistical learning in semi-supervised learning , unsupervised learning, and active learning were expanded . Retrieval of images for further research and related fields , provides a theoretical guidance . From the application point of view, this work solves the small sample image retrieval learning problems , relevance feedback of image sampling issues , as well as pseudo- relevance feedback problem . Thesis work on the promotion of practical image retrieval , there is a certain value .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Retrieval machine
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