Feature Extraction
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Feature Extraction
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Author : Isabelle Guyon
language : en
Publisher: Springer
Release Date : 2008-11-16
Feature Extraction written by Isabelle Guyon and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-11-16 with Computers categories.
This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. Until now there has been insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons.
Feature Extraction And Image Processing For Computer Vision
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Author : Mark Nixon
language : en
Publisher: Academic Press
Release Date : 2012-12-18
Feature Extraction And Image Processing For Computer Vision written by Mark Nixon and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-18 with Computers categories.
Feature Extraction and Image Processing for Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the exemplar code of the algorithms." Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filtering, SURF, PCA-SIFT, moving object detection and tracking, development of symmetry operators, LBP texture analysis, Adaboost, and a new appendix on color models. Coverage of distance measures, feature detectors, wavelets, level sets and texture tutorials has been extended. Named a 2012 Notable Computer Book for Computing Methodologies by Computing Reviews Essential reading for engineers and students working in this cutting-edge field Ideal module text and background reference for courses in image processing and computer vision The only currently available text to concentrate on feature extraction with working implementation and worked through derivation
Feature Extraction Construction And Selection
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Author : Huan Liu
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Feature Extraction Construction And Selection written by Huan Liu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
There is broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data preprocessing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about the research of feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of our endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. Even with today's advanced computer technologies, discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Feature extraction, construction and selection are a set of techniques that transform and simplify data so as to make data mining tasks easier. Feature construction and selection can be viewed as two sides of the representation problem.
A Beginner S Guide To Image Shape Feature Extraction Techniques
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Author : Jyotismita Chaki
language : en
Publisher: CRC Press
Release Date : 2019-07-25
A Beginner S Guide To Image Shape Feature Extraction Techniques written by Jyotismita Chaki and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-25 with Computers categories.
This book emphasizes various image shape feature extraction methods which are necessary for image shape recognition and classification. Focussing on a shape feature extraction technique used in content-based image retrieval (CBIR), it explains different applications of image shape features in the field of content-based image retrieval. Showcasing useful applications and illustrating examples in many interdisciplinary fields, the present book is aimed at researchers and graduate students in electrical engineering, data science, computer science, medicine, and machine learning including medical physics and information technology.
Feature Extraction And Image Processing
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Author : Mark Nixon
language : en
Publisher: Elsevier
Release Date : 2013-10-22
Feature Extraction And Image Processing written by Mark Nixon and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-22 with Computers categories.
Focusing on feature extraction while also covering issues and techniques such as image acquisition, sampling theory, point operations and low-level feature extraction, the authors have a clear and coherent approach that will appeal to a wide range of students and professionals. Ideal module text for courses in artificial intelligence, image processing and computer vision Essential reading for engineers and academics working in this cutting-edge field Supported by free software on a companion website
Image Color Feature Extraction Techniques
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Author : Jyotismita Chaki
language : en
Publisher: Springer Nature
Release Date : 2020-06-03
Image Color Feature Extraction Techniques written by Jyotismita Chaki and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-03 with Technology & Engineering categories.
This book introduces a range of image color feature extraction techniques. Readers are encouraged to try implementing the techniques discussed here on their own, all of which are presented in a very simple and step-by-step manner. In addition, the book can be used as an introduction to image color feature techniques for those who are new to the research field and software. The techniques are very easy to understand as most of them are described with pictorial examples. Not only the techniques themselves, but also their applications are covered. Accordingly, the book offers a valuable guide to these tools, which are a vital component of content-based image retrieval (CBIR).
Unsupervised Feature Extraction Applied To Bioinformatics
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Author : Y-h. Taguchi
language : en
Publisher: Springer Nature
Release Date : 2019-08-23
Unsupervised Feature Extraction Applied To Bioinformatics written by Y-h. Taguchi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-23 with Technology & Engineering categories.
This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. Allows readers to analyze data sets with small samples and many features; Provides a fast algorithm, based upon linear algebra, to analyze big data; Includes several applications to multi-view data analyses, with a focus on bioinformatics.
Texture Feature Extraction Techniques For Image Recognition
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Author : Jyotismita Chaki
language : en
Publisher: Springer Nature
Release Date : 2019-10-24
Texture Feature Extraction Techniques For Image Recognition written by Jyotismita Chaki and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-24 with Technology & Engineering categories.
The book describes various texture feature extraction approaches and texture analysis applications. It introduces and discusses the importance of texture features, and describes various types of texture features like statistical, structural, signal-processed and model-based. It also covers applications related to texture features, such as facial imaging. It is a valuable resource for machine vision researchers and practitioners in different application areas.
Content Based Image Classification
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Author : Rik Das
language : en
Publisher: CRC Press
Release Date : 2020-12-17
Content Based Image Classification written by Rik Das and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-17 with Computers categories.
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/
Prominent Feature Extraction For Sentiment Analysis
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Author : Basant Agarwal
language : en
Publisher: Springer
Release Date : 2015-12-14
Prominent Feature Extraction For Sentiment Analysis written by Basant Agarwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-14 with Medical categories.
The objective of this monograph is to improve the performance of the sentiment analysis model by incorporating the semantic, syntactic and common-sense knowledge. This book proposes a novel semantic concept extraction approach that uses dependency relations between words to extract the features from the text. Proposed approach combines the semantic and common-sense knowledge for the better understanding of the text. In addition, the book aims to extract prominent features from the unstructured text by eliminating the noisy, irrelevant and redundant features. Readers will also discover a proposed method for efficient dimensionality reduction to alleviate the data sparseness problem being faced by machine learning model. Authors pay attention to the four main findings of the book : -Performance of the sentiment analysis can be improved by reducing the redundancy among the features. Experimental results show that minimum Redundancy Maximum Relevance (mRMR) feature selection technique improves the performance of the sentiment analysis by eliminating the redundant features. - Boolean Multinomial Naive Bayes (BMNB) machine learning algorithm with mRMR feature selection technique performs better than Support Vector Machine (SVM) classifier for sentiment analysis. - The problem of data sparseness is alleviated by semantic clustering of features, which in turn improves the performance of the sentiment analysis. - Semantic relations among the words in the text have useful cues for sentiment analysis. Common-sense knowledge in form of ConceptNet ontology acquires knowledge, which provides a better understanding of the text that improves the performance of the sentiment analysis.