Composing Fisher Kernels from Deep Neural Models A Practitioner's Approach /

• Main Authors: Azim, Tayyaba (Author, http://id.loc.gov/vocabulary/relators/aut), Ahmed, Sarah (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Edition: 1st ed. 2018.
• Series: SpringerBriefs in Computer Science,
• Subjects: Pattern recognition.; Signal processing.; Image processing.; Speech processing systems.; Information storage and retrieval.; Mathematical statistics.; Data structures (Computer science).; Artificial intelligence.; Pattern Recognition.; Signal, Image and Speech Processing.; Information Storage and Retrieval.; Probability and Statistics in Computer Science.; Data Storage Representation.; Artificial Intelligence.

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Table of Contents:

• Chapter 1. Kernel Based Learning: A Pragmatic Approach in the Face of New Challenges
• Chapter 2. Fundamentals of Fisher Kernels
• Chapter 3. Training Deep Models and Deriving Fisher Kernels: A Step Wise Approach
• Chapter 4. Large Scale Image Retrieval and Its Challenges
• Chapter 5. Open Source Knowledge Base for Machine Learning Practitioners.