Content Based Image Classification
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Content-Based Image Classification
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® 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/
Computer Vision Methods for Fast Image Classification and Retrieval
The book presents selected methods for accelerating image retrieval and classification in large collections of images using what are referred to as ‘hand-crafted features.’ It introduces readers to novel rapid image description methods based on local and global features, as well as several techniques for comparing images. Developing content-based image comparison, retrieval and classification methods that simulate human visual perception is an arduous and complex process. The book’s main focus is on the application of these methods in a relational database context. The methods presented are suitable for both general-type and medical images. Offering a valuable textbook for upper-level undergraduate or graduate-level courses on computer science or engineering, as well as a guide for computer vision researchers, the book focuses on techniques that work under real-world large-dataset conditions.
Multimedia Information Retrieval and Management
Author: David Feng
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-04-17
Multimedia information technologies, which provide comprehensive and intuitive information for a broad range of applications, have a strong impact on modem life, and have changed our way of learning and thinking. Over the past two decades, there has been an explosive growth in the use of digital multimedia (including audio, video, images and graphics) over the Internet and wireless communication. As the use of digital multimedia increases, effective data storage and management become increasingly important. In fields which use large quantities of data (e. g. audio, video, image and digital libraries; geographical and medical image databases; etc), we need to minimize the volume of data stored while meeting the often conflicting demand for accurate data representation. In addition, the data need to be managed such that it facilitates efficient searching, browsing and cooperative work. This area has been a very active research area in recent years. This book will provide readers with an up-to-date and comprehensive picture of cutting edge technologies in multimedia information retrieval and management, which directly affect our industry, economy and social life The book is divided into two major parts: Technological Fundamentals which covers the core theories of the area; and Applications which describes the broad range of practical uses for this technology.