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Content Based Image Classification And Retrieval Using Support Vector Machine

http://hdl.handle.net/20.500.12678/0000004381
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623cc00c-2bf1-47b5-9141-eb2c50fa7e60
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NJPSC NJPSC 2019 Proceedings-pages-199-206.pdf (979 Kb)
Publication type
Article
Upload type
Publication
Title
Title Content Based Image Classification And Retrieval Using Support Vector Machine
Language en_US
Publication date 2019-03
Authors
Thinzar, Chu
Tin, Hlaing Htake Kaung
Description
Content-based image retrieval (CBIR) systemsare a special type of Information Retrieval (IR) systemwhere the elements in the repository are pictures. IRworks with finding digital resources in large databases.CBIR is developed to retrieve the desired target imagefrom the large collection of images based on thecontents of the given query image. The Contents ofimage can be extracted from any images which arespecified color, shape, texture or any othercharacteristics of images. The system derived two typesof different color features - color moment and colorauto-correlogram. The moments of image can be usedto indicate color distribution of an image which can bedescribed as a probability distribution of colors. Tocalculate the spatial correlation of pairs of colordifferent with distance, color correlogram is introducedin the system. Gabor wavelets are used to expresstexture of natural image. The characteristics of Gaborwavelets are similar to those of human visual features.The system firstly created the features vector with thesethree types of features and used to get higher retrievalresults of system. To gives the better result in retrievalof system and classify the query image, Support VectorMachine classifier is used with the combination ofthese visual features.
Keywords
color moment, color auto-correlogram, Gabor wavelet, feature vector, Support Vector Machine, visual features
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/2318
Journal articles
National Journal of Parallel and Soft Computing
Conference papers
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Thesis/dissertations
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