Content-Based Image Retrieval Using Support Vector Machine in digital image processing techniques

The rapid growth of computer technologies and the ad-vent of the World Wide Web have increased the amount and the complexity of multimedia information. A content-based image retrieval (CBIR) system has been developed as an ef-ficient image retrieval tool, whereby the user can provide their query to the system to allow it to retrieve the user’s desired image from the image database. However, the tradi-tional relevance feedback of CBIR has some limitations that will decrease the performance of the CBIR system, such as the imbalance of training-set problem, classification prob-lem, limited information from user problem, and insuffi-cient trainingset problem. Therefore, in this study, we pro-posed an enhanced relevance-feedback method to support the user query based on the representative image selection and weight ranking of the images retrieved. The support vector machine (SVM) has been used to support the learn-ing process to reduce the semantic gap between the user and the CBIR system. From these experiments, the proposed learning method has enabled users to improve their search results based on the performance of CBIR system. In addi-tion, the experiments also proved that by solving the imbal-ance training set issue, the performance of CBIR could be improved.

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