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Theory and Method Research on Content Based Color Image Retrieval

Author: ChenJingWei
Tutor: WangXiangYang
School: Liaoning Normal University
Course: Applied Computer Technology
Keywords: Content-based image retrieval Relevance feedback Support Vector Machine Expectation Maximization Analysis of orthocomplementation ingredients
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 24
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Abstract


With the rapid development of multimedia information technology and the growing popularity of the network, the increasing number of digital images from all areas of society, image storage and management has become a daunting task. From a huge library of images accurately and quickly find the desired image of the user to become an urgent problem, content-based image retrieval (Content-Based Image Retrieval, CBIR) emerged as important research in the field of multimedia information direction. Thesis, content-based image retrieval core issues, in-depth study, the main contents include: (1) In order to avoid a single visual features can not fully portray the shortcomings of the image content, image retrieval based on color edge features algorithms. The algorithm uses the colored edge contour the Canny detection operator to extract the original image; then construct can fully reflect the the three histogram of the image edge contour information, the color edge weighted color histogram, color edge angle histogram and color edge gradient direction histogram Lastly, FIG.; COMPREHENSIVE three histogram to calculate the degree of similarity between the image and returns a search result. The algorithm is effective to improve the retrieval efficiency. (2) In order to solve the problem of the semantic gap between image low-level visual features and high-level semantic relevance feedback image retrieval algorithm based on the maximum expected parameter estimation integrated support vector machine. Firstly, construct AB-SVM classifier to solve SVM unstable and SVM optimal hyperplane offset problems; construct RS-SVM classifier overflow problem to solve SVM; Finally, the maximum expected parameter estimation method AB- SVM and RS-SVM integration for a stronger performance of the classifier to classify the image. The algorithm has significantly improved the efficiency of the traditional content-based image retrieval. (3) proposed a feature-based reconstruction of relevance feedback image retrieval algorithm. Firstly, the image feature mapped to a high-dimensional kernel space; positive samples orthogonal complementary ingredients to reconstruct the sample images and the characteristics of the test image; Finally, the newly constructed image feature construction classifier. The algorithm for the different properties of the classifier training samples, re-configured to a more easily classify the image feature, effectively improve the classification performance of the classifier.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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