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Image Object Retrieval Based on Content and Semantic Information
Author: RaoFeng
Tutor: SuFei
School: Beijing University of Posts and Telecommunications
Course: Communication and Information System
Keywords: Image semantic concept Image low-level features Support Vector Machine Item Image Retrieval
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 42
Quote: 1
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Abstract
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Along with the development of network and e-commerce , people are no longer satisfied merely through text descriptions to retrieve items , but also hope to become hot items based on content and semantic image retrieval through the image to retrieve items . For baggage retrieval topics studied image low-level features extracted frame structure of the high-level semantics establishment and image retrieval application system , the retrieval algorithm and system framework to establish the research , the following major elements : low-level features in the image extraction in this paper a comprehensive analysis of the characteristics of the image object 's color , texture , shape , and key points in the image retrieval , select improvement HSV space block color histogram and SIFT key points as the underlying characteristics of the image matching , and SIFT key points the projection dimensionality reduction phase cascade retrieve images and other features ; experimental results demonstrate the effectiveness and robustness of the algorithm . Image high-level semantic establish the attributes and characteristics of items related to the definition of the semantic concept set , extract the color feature , Gabor feature edge gradient histograms , and contour features articles semantic concept characterization ; to use the support vector institutions built semantic classifier ; the handling mechanism results priori probability model updating Classifiable ; experimental results show that the accuracy of the semantic retrieval methods . Luggage image retrieval based on semantic image retrieval and content-based image retrieval combined , thus improving the image retrieval speed and accuracy . The system test retrieval accuracy rate of 90.3% , the average retrieval time of 2 seconds per image ; experimental results show the effectiveness of the proposed retrieval algorithm .
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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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