Compression Schemes for Mining Large Datasets by T Ravindra Babu
This book addresses the challenges of data abstraction generation using a least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain.-
NETLAB
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Advanced Algorithmic Approaches to Medical Image Segmentation
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Human Recognition at a Distance in Video
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Nonlinear Eigenproblems in Image Processing and Computer Vision
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Similarity-Based Pattern Analysis and Recognition
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Hyperspectral Image Analysis
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Automatic Digital Document Processing and Management
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Multispectral Satellite Image Understanding
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Guide to Three Dimensional Structure and Motion Factorization
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Handbook of Remote Biometrics
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Fusion in Computer Vision
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Shape Perception in Human and Computer Vision
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Imaging Spectroscopy for Scene Analysis
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Visual Question Answering
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Tensors in Image Processing and Computer Vision
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Handbook of Biometric Anti-Spoofing
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Computer Vision Beyond the Visible Spectrum
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Image Registration
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Statistical and Neural Classifiers
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Contactless 3D Fingerprint Identification
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Hierarchical Perceptual Grouping for Object Recognition
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Adaptive Biometric Systems
Dr. T. Ravindra Babu is a Principal Researcher in the E-Commerce Research Labs at Infosys Ltd., Bangalore, India. Mr. S.V. Subrahmanya is Vice President and Research Fellow at the same organization. Dr. M. Narasimha Murty is a Professor in the Department of Computer Science and Automation at the Indian Institute of Science, Bangalore, India.
| SKU | Unavailable |
| ISBN 13 | 9781447156062 |
| ISBN 10 | 1447156064 |
| Title | Compression Schemes for Mining Large Datasets |
| Author | T Ravindra Babu |
| Series | Advances In Computer Vision And Pattern Recognition |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Springer London Ltd |
| Year published | 2013-12-04 |
| Number of pages | 197 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |