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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
This book presents a systematic study of visual pattern discovery, from unsupervised to semi-supervised manner approaches, and from dealing with a single feature to multiple types of features. Furthermore, it discusses the potential applications of discovering visual patterns for visual data analytics, including visual search, object and scene recognition.
It is intended as a reference book for advanced undergraduates or postgraduate students who are interested in visual data analytics, enabling them to quickly access the research world and acquire a systematic methodology rather than a few isolated techniques to analyze visual data with large variations. It is also inspiring for researchers working in computer vision and pattern recognition fields. Basic knowledge of linear algebra, computer vision and pattern recognition would be helpful to readers.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
This book presents a systematic study of visual pattern discovery, from unsupervised to semi-supervised manner approaches, and from dealing with a single feature to multiple types of features. Furthermore, it discusses the potential applications of discovering visual patterns for visual data analytics, including visual search, object and scene recognition.
It is intended as a reference book for advanced undergraduates or postgraduate students who are interested in visual data analytics, enabling them to quickly access the research world and acquire a systematic methodology rather than a few isolated techniques to analyze visual data with large variations. It is also inspiring for researchers working in computer vision and pattern recognition fields. Basic knowledge of linear algebra, computer vision and pattern recognition would be helpful to readers.