Overview
- Presents a step-by-step approach to deriving a kernel from any probabilistic model belonging to the family of deep networks
- Demonstrates the use of feature compression and selection techniques for reducing the dimensionality of Fisher vectors
- Reviews efficient algorithms for large-scale image retrieval and classification systems, including concrete examples on different datasets
- Provides programming solutions to help machine learning practitioners develop scalable solutions with novel ideas
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (5 chapters)
Keywords
About this book
Reviews
Authors and Affiliations
About the authors
Dr. Tayyaba Azim is an Assistant Professor at the Center for Information Technology, Institute of Management Sciences, Peshawar, Pakistan.
Sarah Ahmed is a current research student enrolled in Masters of Computer Science program at Institute of Management Sciences Peshawar, Pakistan.
She has received her Bachelor’s Degree in Computer Science from Edwardes College, Peshawar,Pakistan. Her areas of interest include: Machine Learning, Computer Vision and Data-Science. Currently, her research work is centered around the feature compression and selection approaches for Fisher vectors derived from deep neural models. Her research paper: "Compression techniques for Deep Fisher Vectors" was awarded the best paper in the area of applications at ICPRAM conference 2017.
Bibliographic Information
Book Title: Composing Fisher Kernels from Deep Neural Models
Book Subtitle: A Practitioner's Approach
Authors: Tayyaba Azim, Sarah Ahmed
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-319-98524-4
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s), under exclusive licence to Springer Nature Switzerland AG 2018
Softcover ISBN: 978-3-319-98523-7Published: 05 September 2018
eBook ISBN: 978-3-319-98524-4Published: 23 August 2018
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XIII, 59
Number of Illustrations: 1 b/w illustrations, 5 illustrations in colour
Topics: Pattern Recognition, Signal, Image and Speech Processing, Information Storage and Retrieval, Probability and Statistics in Computer Science, Data Storage Representation, Artificial Intelligence