Sinossi
Content Based Image Retrieval (CBIR) is a set of techniques for retrieving semantically relevant images from an image database based on automatically derived image features or image content. From early CBIR, it can be seen that the low level features applied in representing images are often global features which are extracted from an entire image. However the performance of these CBIR approaches is still far away from user’s expectation. The problem can be due to the following two reasons. First, it is not unusual that targets, for which the user searches through an image retrieval system, are not images, but visual objects in images. Global features extracted from the image cannot represent the characteristics of objects in these images. Second, features used in most CBIR works are low-level features (colour, texture and shape etc). The semantic gap between low-level feature and high level semantic understanding of images are often hard to bridge.
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Image Segmentation using Clustering Algorithm
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LAP LAMBERT Academic Publishing Okt 2021, 2021
ISBN 10: 6204211161
ISBN 13: 9786204211169
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Content Based Image Retrieval (CBIR) is a set of techniques for retrieving semantically relevant images from an image database based on automatically derived image features or image content. From early CBIR, it can be seen that the low level features applied in representing images are often global features which are extracted from an entire image. However the performance of these CBIR approaches is still far away from user's expectation. The problem can be due to the following two reasons. First, it is not unusual that targets, for which the user searches through an image retrieval system, are not images, but visual objects in images. Global features extracted from the image cannot represent the characteristics of objects in these images. Second, features used in most CBIR works are low-level features (colour, texture and shape etc). The semantic gap between low-level feature and high level semantic understanding of images are often hard to bridge. 68 pp. Englisch. Codice articolo 9786204211169
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Image Segmentation using Clustering Algorithm
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ISBN 13: 9786204211169
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Image Segmentation using Clustering Algorithm
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LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6204211161
ISBN 13: 9786204211169
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Image Segmentation using Clustering Algorithm
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ISBN 10: 6204211161
ISBN 13: 9786204211169
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Image Segmentation using Clustering Algorithm
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LAP LAMBERT Academic Publishing, 2021
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ISBN 13: 9786204211169
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: K. SakthivelK.Sakthivel received Ph.D. degree in Information and Communication Engineering Anna University, Chennai in 2012. Currently working as professor in the department of Computer Science and Engineering at K.S.Rangasamy Colleg. Codice articolo 526153390
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Image Segmentation using Clustering Algorithm
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LAP LAMBERT Academic Publishing Okt 2021, 2021
ISBN 10: 6204211161
ISBN 13: 9786204211169
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Content Based Image Retrieval (CBIR) is a set of techniques for retrieving semantically relevant images from an image database based on automatically derived image features or image content. From early CBIR, it can be seen that the low level features applied in representing images are often global features which are extracted from an entire image. However the performance of these CBIR approaches is still far away from user's expectation. The problem can be due to the following two reasons. First, it is not unusual that targets, for which the user searches through an image retrieval system, are not images, but visual objects in images. Global features extracted from the image cannot represent the characteristics of objects in these images. Second, features used in most CBIR works are low-level features (colour, texture and shape etc). The semantic gap between low-level feature and high level semantic understanding of images are often hard to bridge.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 68 pp. Englisch. Codice articolo 9786204211169
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Image Segmentation using Clustering Algorithm : Hierarchical Agglomerative K-Means
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LAP LAMBERT Academic Publishing, 2021
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Content Based Image Retrieval (CBIR) is a set of techniques for retrieving semantically relevant images from an image database based on automatically derived image features or image content. From early CBIR, it can be seen that the low level features applied in representing images are often global features which are extracted from an entire image. However the performance of these CBIR approaches is still far away from user's expectation. The problem can be due to the following two reasons. First, it is not unusual that targets, for which the user searches through an image retrieval system, are not images, but visual objects in images. Global features extracted from the image cannot represent the characteristics of objects in these images. Second, features used in most CBIR works are low-level features (colour, texture and shape etc). The semantic gap between low-level feature and high level semantic understanding of images are often hard to bridge. Codice articolo 9786204211169
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Image Segmentation using Clustering Algorithm | Hierarchical Agglomerative K-Means
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Taschenbuch. Condizione: Neu. Image Segmentation using Clustering Algorithm | Hierarchical Agglomerative K-Means | Sakthivel K. (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204211169 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 120791669
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