Sinopsis
Each image in a Content Based Image Retrieval (CBIR) system is represented by its features such as colour, texture and shape. These three groups of features are stored in the feature vector. Therefore, each image managed by the CBIR system is associated with one or more feature vectors. This book presents an improved approach to select significant features from the huge image feature vector. The concept behind this research is that it is possible to extract image feature relational patterns in an image feature vector database. After which, these relational patterns are used to generate rules and improve the retrieval results for a CBIR system. In addition, this research proposes a CBIR system utilising the Rough Set instead of deterministic and crisp methods. In this research, Rough Set rules are evaluated with noisy images. Also, in order to have a more accurate classifier in the CBIR system, the classifier is proposed to be based on the Rough Set and Support Vector Machine (SVM) in this research.
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Acerca del autor
Dr. Maryam Shahabi has a PhD degree in Information Technology and Master Degree in Artificial Intelligence. She worked and taught in the Information Technology environment for the past seven years in both industry and in academic environments. Her research interests are Image retrieval, soft computing, Artificial intelligence and machine learning.
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Image Retrieval System and Rough Set Theory
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LAP LAMBERT Academic Publishing Nov 2016, 2016
ISBN 10: 3330007370
ISBN 13: 9783330007376
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Each image in a Content Based Image Retrieval (CBIR) system is represented by its features such as colour, texture and shape. These three groups of features are stored in the feature vector. Therefore, each image managed by the CBIR system is associated with one or more feature vectors. This book presents an improved approach to select significant features from the huge image feature vector. The concept behind this research is that it is possible to extract image feature relational patterns in an image feature vector database. After which, these relational patterns are used to generate rules and improve the retrieval results for a CBIR system. In addition, this research proposes a CBIR system utilising the Rough Set instead of deterministic and crisp methods. In this research, Rough Set rules are evaluated with noisy images. Also, in order to have a more accurate classifier in the CBIR system, the classifier is proposed to be based on the Rough Set and Support Vector Machine (SVM) in this research. 180 pp. Englisch. Nº de ref. del artículo: 9783330007376
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Image Retrieval System and Rough Set Theory
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ISBN 10: 3330007370
ISBN 13: 9783330007376
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Shahabi Lotfabadi MaryamDr. Maryam Shahabi has a PhD degree in Information Technology and Master Degree in Artificial Intelligence. She worked and taught in the Information Technology environment for the past seven years in both indu. Nº de ref. del artículo: 151233822
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Image Retrieval System and Rough Set Theory
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ISBN 10: 3330007370
ISBN 13: 9783330007376
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Paperback. Condición: Brand New. 01 edition. 180 pages. 8.66x5.91x0.41 inches. In Stock. Nº de ref. del artículo: 3330007370
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Image Retrieval System and Rough Set Theory
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LAP LAMBERT Academic Publishing Nov 2016, 2016
ISBN 10: 3330007370
ISBN 13: 9783330007376
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Each image in a Content Based Image Retrieval (CBIR) system is represented by its features such as colour, texture and shape. These three groups of features are stored in the feature vector. Therefore, each image managed by the CBIR system is associated with one or more feature vectors. This book presents an improved approach to select significant features from the huge image feature vector. The concept behind this research is that it is possible to extract image feature relational patterns in an image feature vector database. After which, these relational patterns are used to generate rules and improve the retrieval results for a CBIR system. In addition, this research proposes a CBIR system utilising the Rough Set instead of deterministic and crisp methods. In this research, Rough Set rules are evaluated with noisy images. Also, in order to have a more accurate classifier in the CBIR system, the classifier is proposed to be based on the Rough Set and Support Vector Machine (SVM) in this research.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 180 pp. Englisch. Nº de ref. del artículo: 9783330007376
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Image Retrieval System and Rough Set Theory
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Taschenbuch. Condición: Neu. Image Retrieval System and Rough Set Theory | Maryam Shahabi Lotfabadi | Taschenbuch | 180 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783330007376 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Nº de ref. del artículo: 107977538
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Image Retrieval System and Rough Set Theory
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ISBN 10: 3330007370
ISBN 13: 9783330007376
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Each image in a Content Based Image Retrieval (CBIR) system is represented by its features such as colour, texture and shape. These three groups of features are stored in the feature vector. Therefore, each image managed by the CBIR system is associated with one or more feature vectors. This book presents an improved approach to select significant features from the huge image feature vector. The concept behind this research is that it is possible to extract image feature relational patterns in an image feature vector database. After which, these relational patterns are used to generate rules and improve the retrieval results for a CBIR system. In addition, this research proposes a CBIR system utilising the Rough Set instead of deterministic and crisp methods. In this research, Rough Set rules are evaluated with noisy images. Also, in order to have a more accurate classifier in the CBIR system, the classifier is proposed to be based on the Rough Set and Support Vector Machine (SVM) in this research. Nº de ref. del artículo: 9783330007376
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Image Retrieval System and Rough Set Theory
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ISBN 10: 3330007370
ISBN 13: 9783330007376
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paperback. Condición: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book. Nº de ref. del artículo: ERICA82933300073706
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