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008 210108s2020 flu eo 000 0 eng d
040 _aOCoLC-P
_beng
_erda
_epn
_cOCoLC-P
020 _a9780429352928
_q(electronic bk.)
020 _a0429352921
_q(electronic bk.)
020 _a9781000280470
_q(electronic bk. : PDF)
020 _a1000280470
_q(electronic bk. : PDF)
020 _z9780367371609
020 _a9781000280715
_q(electronic bk. : EPUB)
020 _a1000280713
_q(electronic bk. : EPUB)
020 _a9781000280593
_q(electronic bk. : Mobipocket)
020 _a1000280594
_q(electronic bk. : Mobipocket)
020 _z9780367639280
020 _z036737160X
024 8 _a10.1201/9780429352928
_2doi
035 _a(OCoLC)1229166014
_z(OCoLC)1202438953
_z(OCoLC)1202474310
_z(OCoLC)1202478154
_z(OCoLC)1202599751
_z(OCoLC)1230505336
035 _a(OCoLC-P)1229166014
050 4 _aZ699.5.P53
072 7 _aCOM
_x016000
_2bisacsh
072 7 _aCOM
_x059000
_2bisacsh
072 7 _aCOM
_x037000
_2bisacsh
072 7 _aUYQ
_2bicssc
082 0 4 _a025.040285/66
_223
100 1 _aDas, Rik,
_d1978-
_eauthor.
_919301
245 1 0 _aContent-based image classification :
_befficient machine learning using robust feature extraction techniques /
_cRik Das.
264 1 _aBoca Raton, FL :
_bCRC Press,
_c2020.
300 _a1 online resource (xvi, 180 pages).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
505 0 _a1.Introduction to Content Based Image Classification. 2. A Review of Hand-crafted Feature Extraction Techniques for Content Based Image Classification. 3. Content Based Feature Extraction: Color Averaging. 4. Content Based Feature Extraction: Image Binarization. 5. Content Based Feature Extraction: Image Transforms. 6. Content Based Feature Extraction: Morphological Operators.7. Content Based Feature Extraction: Texture Components. 8. Fusion Based Classification: A Comparison of Early Fusion and Late Fusion Architecture for Content Based Features. 9. Future Directions: A Journey from Handcrafted Techniques to Representation Learning. 10. WEKA: Beginners' Tutorial
520 _aContent-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/
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aContent-based image retrieval.
_914447
650 0 _aMachine learning.
_91831
650 7 _aCOMPUTERS / Computer Vision & Pattern Recognition
_2bisacsh
_914865
650 7 _aCOMPUTERS / Computer Engineering
_2bisacsh
_94770
650 7 _aCOMPUTERS / Machine Theory
_2bisacsh
_919302
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9780429352928
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942 _cEBK
999 _c72070
_d72070