Image Retrieval System: An Indexing Technique Using Annotation for Improved Markovian Model Based Image Retrieval System - Softcover
Synopsis
Online image retrieval system is more important in the real world environment where the users submitted their queries to retrieve the images. The main aim of mining is extracting valuable information from huge volume of textual or image datasets. The user queries are mined to enhance the web searching which is extremely useful to improve the retrieval effectiveness. The image retrieval accuracy is measured based on the user satisfaction level. Automatic annotation is utilized for operating over high dimensional text documents or image contents. Different online image retrieval techniques are available like Latent Semantic Indexing (LSI), Markovian Semantic Indexing (MSI) etc., but these techniques cannot retrieve the images accurately due to the lack of semantic knowledge about the user submitted high level keywords. Moreover the LSI and MSI have less image retrieval accuracy. Hence, this research work focussed on the improvement of the accuracy of the image retrieval system.
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Image Retrieval System
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LAP LAMBERT Academic Publishing Okt 2019, 2019
ISBN 10: 6200287368
ISBN 13: 9786200287366
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Online image retrieval system is more important in the real world environment where the users submitted their queries to retrieve the images. The main aim of mining is extracting valuable information from huge volume of textual or image datasets. The user queries are mined to enhance the web searching which is extremely useful to improve the retrieval effectiveness. The image retrieval accuracy is measured based on the user satisfaction level. Automatic annotation is utilized for operating over high dimensional text documents or image contents. Different online image retrieval techniques are available like Latent Semantic Indexing (LSI), Markovian Semantic Indexing (MSI) etc., but these techniques cannot retrieve the images accurately due to the lack of semantic knowledge about the user submitted high level keywords. Moreover the LSI and MSI have less image retrieval accuracy. Hence, this research work focussed on the improvement of the accuracy of the image retrieval system. 112 pp. Englisch. Seller Inventory # 9786200287366
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Image Retrieval System
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Seenivasan SangeethaDr. Sangeetha Seenivasan is Head and Assistant Professor, Department of CA and IT. She has completed her B.Sc (CS)., M.Sc (CT)., M.Phil. and Ph.D. in Bharathiar University. She got Gold Medal on her PG Degree. She. Seller Inventory # 385887337
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Image Retrieval System
Published by
LAP LAMBERT Academic Publishing Okt 2019, 2019
ISBN 10: 6200287368
ISBN 13: 9786200287366
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Online image retrieval system is more important in the real world environment where the users submitted their queries to retrieve the images. The main aim of mining is extracting valuable information from huge volume of textual or image datasets. The user queries are mined to enhance the web searching which is extremely useful to improve the retrieval effectiveness. The image retrieval accuracy is measured based on the user satisfaction level. Automatic annotation is utilized for operating over high dimensional text documents or image contents. Different online image retrieval techniques are available like Latent Semantic Indexing (LSI), Markovian Semantic Indexing (MSI) etc., but these techniques cannot retrieve the images accurately due to the lack of semantic knowledge about the user submitted high level keywords. Moreover the LSI and MSI have less image retrieval accuracy. Hence, this research work focussed on the improvement of the accuracy of the image retrieval system.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 112 pp. Englisch. Seller Inventory # 9786200287366
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Image Retrieval System : An Indexing Technique Using Annotation for Improved Markovian Model Based Image Retrieval System
Published by
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200287368
ISBN 13: 9786200287366
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Online image retrieval system is more important in the real world environment where the users submitted their queries to retrieve the images. The main aim of mining is extracting valuable information from huge volume of textual or image datasets. The user queries are mined to enhance the web searching which is extremely useful to improve the retrieval effectiveness. The image retrieval accuracy is measured based on the user satisfaction level. Automatic annotation is utilized for operating over high dimensional text documents or image contents. Different online image retrieval techniques are available like Latent Semantic Indexing (LSI), Markovian Semantic Indexing (MSI) etc., but these techniques cannot retrieve the images accurately due to the lack of semantic knowledge about the user submitted high level keywords. Moreover the LSI and MSI have less image retrieval accuracy. Hence, this research work focussed on the improvement of the accuracy of the image retrieval system. Seller Inventory # 9786200287366
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Image Retrieval System | An Indexing Technique Using Annotation for Improved Markovian Model Based Image Retrieval System
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Taschenbuch. Condition: Neu. Image Retrieval System | An Indexing Technique Using Annotation for Improved Markovian Model Based Image Retrieval System | Sangeetha Seenivasan | Taschenbuch | 112 S. | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786200287366 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 118436983
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Image Retrieval System: An Indexing Technique Using Annotation for Improved Markovian Model Based Image Retrieval System
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ISBN 13: 9786200287366
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