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024 7 _a10.1007/978-981-16-8976-5
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100 1 _aLi, Jinxing.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aInformation Fusion
_h[electronic resource] :
_bMachine Learning Methods /
_cby Jinxing Li, Bob Zhang, David Zhang.
250 _a1st ed. 2022.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2022.
300 _aXXVI, 260 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 1. Introduction -- Chapter 2. Information fusion based on sparse/collaborative representation -- Chapter 3. Information fusion based on gaussian process latent variable model -- Chapter 4. Information fusion based on multi-view and multifeature earning -- Chapter 5. Information fusion based on metric learning -- Chapter 6. Information fusion based on score/weight classifier fusion -- Chapter 7. Information fusion based on deep learning -- Chapter 8. Conclusion.
520 _aIn the big data era, increasing information can be extracted from the same source object or scene. For instance, a person can be verified based on their fingerprint, palm print, or iris information, and a given image can be represented by various types of features, including its texture, color, shape, etc. These multiple types of data extracted from a single object are called multi-view, multi-modal or multi-feature data. Many works have demonstrated that the utilization of all available information at multiple abstraction levels (measurements, features, decisions) helps to obtain more complex, reliable and accurate information and to maximize performance in a range of applications. This book provides an overview of information fusion technologies, state-of-the-art techniques and their applications. It covers a variety of essential information fusion methods based on different techniques, including sparse/collaborative representation, kernel strategy,Bayesian models, metric learning, weight/classifier methods, and deep learning. The typical applications of these proposed fusion approaches are also presented, including image classification, domain adaptation, disease detection, image restoration, etc. This book will benefit all researchers, professionals and graduate students in the fields of computer vision, pattern recognition, biometrics applications, etc. Furthermore, it offers a valuable resource for interdisciplinary research.
650 0 _aImage processing
_xDigital techniques.
650 0 _aComputer vision.
650 0 _aArtificial intelligence.
650 0 _aData mining.
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
650 2 4 _aArtificial Intelligence.
650 2 4 _aData Mining and Knowledge Discovery.
700 1 _aZhang, Bob.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aZhang, David.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811689758
776 0 8 _iPrinted edition:
_z9789811689772
776 0 8 _iPrinted edition:
_z9789811689789
856 4 0 _uhttps://doi.org/10.1007/978-981-16-8976-5
912 _aZDB-2-SCS
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999 _c179281
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