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SVM Based Image Content Retrieval Research

Posted on:2011-06-01Degree:MasterType:Thesis
Country:ChinaCandidate:C G DengFull Text:PDF
GTID:2178360308955459Subject:Circuits and Systems
Abstract/Summary:PDF Full Text Request
With"interactive systems"and"human-in-loop"structures, relevance feedback is an important tool to improve the performance of content based image retrieval. It can bridge the semantic gap between high-level concepts and low-level visual features. Especially, SVM based relevance feedback greatly improve the performance of retrieval system with its good generalization.However, the SVM-RF's performance may become poor because of the following three reasons: 1) SVM classifier is unstable with small training samples; 2) SVM's optimal hyper-plane may be biased when the positive feedback samples are much less than the negative feedback samples; 3) Over-fitting due to that the feature dimension size is larger than the size of the training set; 4) Algorithm's time cost is strictly limited, as the user takes part in the retrieval process. In response to these problems, this paper carries out the following research:Firstly, the key technologies of CBIR are described and analyzed.Secondly, a new algorithm is proposed. Combining multiple features and training multiple SVM classifiers, it gets a better grasp of image retrieval user's subjective intent.Thirdly, an asymmetric bagging based fuzzy support vector machine (AB-FSVM) is proposed. An asymmetric bagging is made to negative samples, and then based on fuzzy theory and SVM, the retrieval images are gotten. This can solve the small training samples and samples'asymmetry problems. Besides, with relatively less feature dimension size, over-fitting problems can be eased. Experiments show that compared with existing algorithms, the retrieval performance has been greatly improved with only a slight increase in the time-consuming.Based on the above work, a local database-oriented image retrieval system is built . And the performance of the proposed algorithm is verified.
Keywords/Search Tags:content based image retrieval (CBIR), relevance feedback (RF), support vector machine (SVM), fuzzy support vector machine (FSVM)
PDF Full Text Request
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