Content-Based Image Classification: Efficient Machine Learning using Robust Feature Extraction Techniques

Das, Rik

ISBN 10: 036737160X / ISBN 13: 9780367371609

Edité par Chapman and Hall/CRC, 2020

A propos de cet article

N° de réf. du vendeur 380536829

Détails bibliographiques

• Titre: Content-Based Image Classification: Efficient Machine Learning using Robust Feature Extraction Techniques
• Éditeur: Chapman and Hall/CRC
• Date de parution: 2020
• Langue: anglais
• ISBN à 10 chiffres: 036737160X
• ISBN à 13 chiffres: 9780367371609
• Reliure: Couverture rigide
• État de l'article: New

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Synopsis

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.

À propos de l?auteur

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.