| Indexing of Content-Based Image Retrieval System with Image Understanding Approach |
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| 作者姓名： | 李学龙 刘政凯 俞能海 |
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| 作者单位： | Li Xuelong，Liu Zhengkai & Yu Nenghai Information Processing Center，University of Science and Technology of China，Hefei 230027，P. R. China |
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| 基金项目： | ThisprojectwassupportedbytheNaturalScienceFoundationofAnhuiProvince (0 30 4 2 30 7) |
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| 摘 要： | 1 .INTRODUCTIONMuchattentionhasbeenpaidtoCBIR (content basedimageretrieval)inrecentyears1 2 ] ,andvarioustech niqueshavebeenappliedtoretrieveimagesbasedonlow levelfeatures,includingcolor,texture,sketchandshape .These previouseffortshave producedmanyinterestingresults4～ 9] basedondifferentimagefeaturesandmetrics.Especially ,theCBIRsystemsbasedonrelevancefeedback ,keywordsannotationandothernovelmethodsworkwellundermanykindsofimagequeries.However,therearestillsomeproblemsconcern ingCB…
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Indexing of Content-Based Image Retrieval System with Image Understanding Approach |
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| Li Xuelong,Liu Zhengkai & Yu Nenghai Information Processing Center,University of Science and Technology of China,Hefei ,P. R. China.Indexing of Content-Based Image Retrieval System with Image Understanding Approach[J].Journal of Systems Engineering and Electronics,2003,14(2). |
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| Authors: | Li Xuelong Liu Zhengkai & Yu Nenghai Information Processing Center University of Science and Technology of China Hefei P R China |
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| Institution: | Information Processing Center, University of Science and Technology of China, Hefei 230027, P. R. China |
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| Abstract: | This paper presents a novel efficient semantic image classification algorithm for high-level feature indexing of high-dimension image database. Experiments show that the algorithm performs well. The size of the train set and the test set is 7 537 and 5 000 respectively. Based on this theory, another ground is built with 12,000 images, which are divided into three classes: city, landscape and person, the total result of the classifications is 88.92%, meanwhile, some preliminary results are presented for image understanding based on semantic image classification and low level features. The groundtruth for the experiments is built with the images from Corel database, photos and some famous face databases. |
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| Keywords: | Content-based image retrieval Image classification Image indexing |
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