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Content-Based Image Classification - Efficient Machine Learning Using Robust Feature Extraction Techniques (Hardcover)
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Content-Based Image Classification - Efficient Machine Learning Using Robust Feature Extraction Techniques (Hardcover)
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Content-Based Image Classification: Efficient Machine Learning
Using Robust Feature Extraction Techniques is a comprehensive guide
to research with invaluable image data. Social Science Research
Network has revealed that 65% of people are visual learners.
Research data provided by Hyerle (2000) has clearly shown 90% of
information in the human brain is visual. Thus, it is no wonder
that visual information processing in the brain is 60,000 times
faster than text-based information (3M Corporation, 2001).
Recently, we have witnessed a significant surge in conversing with
images due to the popularity of social networking platforms. The
other reason for embracing usage of image data is the mass
availability of high-resolution cellphone cameras. Wide usage of
image data in diversified application areas including medical
science, media, sports, remote sensing, and so on, has spurred the
need for further research in optimizing archival, maintenance, and
retrieval of appropriate image content to leverage data-driven
decision-making. This book demonstrates several techniques of image
processing to represent image data in a desired format for
information identification. It discusses the application of machine
learning and deep learning for identifying and categorizing
appropriate image data helpful in designing automated decision
support systems. The book offers comprehensive coverage of the most
essential topics, including: Image feature extraction with novel
handcrafted techniques (traditional feature extraction) Image
feature extraction with automated techniques (representation
learning with CNNs) Significance of fusion-based approaches in
enhancing classification accuracy MATLAB (R) codes for implementing
the techniques Use of the Open Access data mining tool WEKA for
multiple tasks The book is intended for budding researchers,
technocrats, engineering students, and machine learning/deep
learning enthusiasts who are willing to start their computer vision
journey with content-based image recognition. The readers will get
a clear picture of the essentials for transforming the image data
into valuable means for insight generation. Readers will learn
coding techniques necessary to propose novel mechanisms and
disruptive approaches. The WEKA guide provided is beneficial for
those uncomfortable coding for machine learning algorithms. The
WEKA tool assists the learner in implementing machine learning
algorithms with the click of a button. Thus, this book will be a
stepping-stone for your machine learning journey. Please visit the
author's website for any further guidance at
https://www.rikdas.com/
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