Rik Das

Content-Based Image Classification (eBook, ePUB)

Efficient Machine Learning Using Robust Feature Extraction Techniques

• Format: ePub

Produktbeschreibung

This book is a comprehensive guide to initiate and excel in researching with invaluable image data. This book has demonstrated several techniques of image processing to represent image data in desired format for information identification.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 196
• Erscheinungstermin: 17. Dezember 2020
• Englisch
• ISBN-13: 9781000280715
• Artikelnr.: 60468171

Autorenporträt

Rik Das is a PhD (Tech.) and M.Tech. in Information Technology from the University of Calcutta, India. He is also a B.E. in Information Technology from the University of Burdwan, India. Rik has filed and published two Indian patents consecutively during the year 2018 and 2019 and has over 40 International publications till date. He has collaborated with professionals from leading multinational software companies and with Professors and researchers of Universities in India and abroad for research work in the domain of content based image classification. Rik has over 16 years of experience in research and academia and is currently an Assistant Professor for the Program of Information Technology at Xavier Institute of Social Service (XISS), Ranchi, India.

Rik is appointed as a Distinguished Speaker of the Association of Computing Machinery (ACM), New York, USA. He is featured in uLektz Wall of Fame as one of the "Top 50 Tech Savvy Academicians in Higher Education across India" for the year 2019. He is also a Member of International Advisory Committee of AI-Forum, UK. Rik has founded a YouTube channel named 'Curious Neuron' to disseminate knowledge and information to larger communities in the domain of machine learning, research and development and open source programming languages.

Inhaltsangabe

1. Introduction to Content Based Image Classification. 2. A Review of
Hand-crafted Feature Extraction Techniques for Content Based Image
Classification. 3. Content Based Feature Extraction: Color Averaging. 4.
Content Based Feature Extraction: Image Binarization. 5. Content Based
Feature Extraction: Image Transforms. 6. Content Based Feature Extraction:
Morphological Operators. 7. Content Based Feature Extraction: Texture
Components. 8. Fusion Based Classification: A Comparison of Early Fusion
and Late Fusion Architecture for Content Based Features. 9. Future
Directions: A Journey from Handcrafted Techniques to Representation
Learning. 10. WEKA: Beginners' Tutorial