Produktbild
Tayyaba Azim

Composing Fisher Kernels from Deep Neural Models

A Practitioner's Approach

Ebook (PDF Format)

This book shows machine learning enthusiasts and practitioners how to get the best of both worlds by deriving Fisher kernels from deep learning models. In addition, the book shares insight on how to store and retrieve large-dimensional Fisher vectors using feature selection and compression techniques. Feature selection and feature compression are two of the most popular off-the-shelf methods for reducing data's high-dimensional memory footprint and thus making it suitable for large-scale visual retrieval and classification. Kernel methods long remained the de facto standard for solving large-scale object classification tasks using low-level f… Mehr

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Produktdetails


Weitere Autoren: Ahmed, Sarah
  • ISBN: 978-3-319-98524-4
  • EAN: 9783319985244
  • Produktnummer: 28515953
  • Verlag: Springer-Verlag GmbH
  • Sprache: Englisch
  • Erscheinungsjahr: 2018
  • Seitenangabe: 59 S.
  • Plattform: PDF
  • Masse: 2'445 KB
  • Auflage: 1st ed. 2018
  • Abbildungen: 1 schwarz-weiße und 5 farbige Abbildungen, Bibliographie

Über den Autor


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. 

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