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Composing Fisher Kernels from Deep Neural Models

A Practitioner's Approach

  • Tayyaba Azim
  • Sarah Ahmed

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Tayyaba Azim, Sarah Ahmed
    Pages 9-17
  3. Tayyaba Azim, Sarah Ahmed
    Pages 33-46

About this book

Introduction

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 features, until the revival of deep models in 2006. Later, they made a comeback with improved Fisher vectors in 2010. However, their supremacy was always challenged by various versions of deep models, now considered to be the state of the art for solving various machine learning and computer vision tasks. Although the two research paradigms differ significantly, the excellent performance of Fisher kernels on the Image Net large-scale object classification dataset has caught the attention of numerous kernel practitioners, and many have drawn parallels between the two frameworks for improving the empirical performance on benchmark classification tasks. Exploring concrete examples on different data sets, the book compares the computational and statistical aspects of different dimensionality reduction approaches and identifies metrics to show which approach is superior to the other for Fisher vector encodings. It also provides references to some of the most useful resources that could provide practitioners and machine learning enthusiasts a quick start for learning and implementing a variety of deep learning models and kernel functions.

Keywords

Deep Models Fisher Vectors Large Scale Information Retrieval Feature Compression Techniques Feature Selection Techniques

Authors and affiliations

  • Tayyaba Azim
    • 1
  • Sarah Ahmed
    • 2
  1. 1.Center of Excellence in ITInstitute of Management SciencesPeshawarPakistan
  2. 2.Institute of Management SciencesPeshawarPakistan

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-98524-4
  • Copyright Information The Author(s), under exclusive licence to Springer Nature Switzerland AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-98523-7
  • Online ISBN 978-3-319-98524-4
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site
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