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010 a| 2020030222;

020 a| 9780367371609q| (hardback);

020 z| 9780429352928q| (ebook);

035 a| (WaSeSS)ssj0002409201;

040 a| LBSOR/DLCb| engc| DLCd| WaSeSS;

042 a| pcc;

049 a| EREEa| NEHH;

050 00 a| TA1650b| .D376 2021;

082 00 a| 006.4/202856312| 23;

100 1 a| Das, Rik,d| 1978-?| UNAUTHORIZED;

245 10 a| Content-based image classificationh| [electronic resource] :b| efficient machine learning using robust feature extraction techniques /c| Rik Das.;

250 a| First edition.;

260 a| Boca Raton :b| C&HCRC Press,c| 2021.;

263 a| 2010;

300 a| pages cm;

504 a| Includes bibliographical references and index.;

505 0 a| Introduction to content based image classification -- A review of hand-crafted feature extraction techniques for content based image classification -- Content based feature extraction : color averaging -- Content based feature extraction : image binarization -- Content based feature extraction : image transforms -- Content based feature extraction : morphological operators -- Content based feature extraction : texture components -- Fusion based classification : a comparison of early fusion and late fusion architecture for content based features -- Future directions : a journey from handcrafted techniques to representation learning -- WEKA : beginners' tutorial.;

506 a| Available only to authorized users.;

520 a| "Content-Based Image Classification Efficient Machine Learning using Robust Feature Extraction Techniques is a comprehensive guide to initiate and excel in researching with invaluable image data. Social Science Research Network has revealed the fact that sixty five percent of us are visual learners. Research data provided by Hyerle (2000) has clearly shown ninety percent of information in our brain is visual. Thus, it is no wonder that processing of visual information in brain is 60,000 times faster than text based information (3M Corporation, 2001). Recent times have witnessed significant surge in conversing with images with popularity of social networking platforms. The other reason of embracing extensive usage of image data is easy availability of image capturing devices in the form of high resolution cell phone cameras. Extensive application of image data in diversified application areas including, medical science, media, sports, remote sensing and so on has stimulated the requirement of further research in optimizing archival, maintenance and retrieval of appropriate image content to leverage data driven decision making. This book has demonstrated several techniques of image processing to represent image data in desired format for information identification. It has discussed 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: Different Open Access Image Datasets to start your Machine Learning Journey 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 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 of insight generation. The book will make the reader adept with coding tricks necessary to propose novel mechanisms and also to enhance state-of-the-art with disruptive approaches. The Weka guide provided in the book can prove itself beneficial for those who are not comfortable with coding for application of machine learning algorithm. The Weka tool will assist the learner to implement machine learning algorithms with the click of a button. Thus, the book is going to be your stepping stone for your machine learning journey. You may visit the author's website to get in touch for any further guidance required (Website: https://www.rikdas.com/)"--c| Provided by publisher.;

538 a| Mode of access: World Wide Web;

650 0 a| Optical pattern recognition.=| ^A46474;

650 0 a| Images, Photographicx| Classification.=| ^A370167;

650 0 a| Image analysisx| Data processing.=| ^A368011;

650 0 a| Image data mining.?| UNAUTHORIZED;

650 0 a| Machine learning.=| ^A145211;

655 0 a| Electronic books.=| ^A491897;

856 40 z| Full text available from Taylor & Francis eBooksu| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fwww.taylorfrancis.com%2Fbooks%2F9780429352928;

856 40 z| Full text available from Ebook Central - Academic Completeu| https://ebookcentral.proquest.com/lib/eastcarolina/detail.action?docID=6379676;

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999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6455067-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 7/20/2024x| EBOOKz| JERESOURCE;

Content-based image classification efficient machine learning using robust feature extraction techniques / Rik Das.

Author/creator	Das, Rik, 1978-
Format	Electronic
Edition	First edition.
Publication Info	Boca Raton : C&HCRC Press, 2021.
Description	pages cm
Supplemental Content	Full text available from Taylor & Francis eBooks
Supplemental Content	Full text available from Ebook Central - Academic Complete
Subjects	Optical pattern recognition. Images, Photographic--Classification. Image analysis--Data processing. Image data mining. Machine learning.

Contents	Introduction to content based image classification -- A review of hand-crafted feature extraction techniques for content based image classification -- Content based feature extraction : color averaging -- Content based feature extraction : image binarization -- Content based feature extraction : image transforms -- Content based feature extraction : morphological operators -- Content based feature extraction : texture components -- Fusion based classification : a comparison of early fusion and late fusion architecture for content based features -- Future directions : a journey from handcrafted techniques to representation learning -- WEKA : beginners' tutorial.
Abstract	"Content-Based Image Classification Efficient Machine Learning using Robust Feature Extraction Techniques is a comprehensive guide to initiate and excel in researching with invaluable image data. Social Science Research Network has revealed the fact that sixty five percent of us are visual learners. Research data provided by Hyerle (2000) has clearly shown ninety percent of information in our brain is visual. Thus, it is no wonder that processing of visual information in brain is 60,000 times faster than text based information (3M Corporation, 2001). Recent times have witnessed significant surge in conversing with images with popularity of social networking platforms. The other reason of embracing extensive usage of image data is easy availability of image capturing devices in the form of high resolution cell phone cameras. Extensive application of image data in diversified application areas including, medical science, media, sports, remote sensing and so on has stimulated the requirement of further research in optimizing archival, maintenance and retrieval of appropriate image content to leverage data driven decision making. This book has demonstrated several techniques of image processing to represent image data in desired format for information identification. It has discussed 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: Different Open Access Image Datasets to start your Machine Learning Journey 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 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 of insight generation. The book will make the reader adept with coding tricks necessary to propose novel mechanisms and also to enhance state-of-the-art with disruptive approaches. The Weka guide provided in the book can prove itself beneficial for those who are not comfortable with coding for application of machine learning algorithm. The Weka tool will assist the learner to implement machine learning algorithms with the click of a button. Thus, the book is going to be your stepping stone for your machine learning journey. You may visit the author's website to get in touch for any further guidance required (Website: https://www.rikdas.com/)"-- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2020030222
ISBN	9780367371609 (hardback)
ISBN	(ebook)

Content-based image classification efficient machine learning using robust feature extraction techniques / Rik Das.

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