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020 _a9783319303673
_9978-3-319-30367-3
024 7 _a10.1007/978-3-319-30367-3
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aIonescu, Radu Tudor.
_eauthor.
245 1 0 _aKnowledge Transfer between Computer Vision and Text Mining
_h[electronic resource] :
_bSimilarity-based Learning Approaches /
_cby Radu Tudor Ionescu, Marius Popescu.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXXIV, 250 p. 42 illus., 33 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvances in Computer Vision and Pattern Recognition,
_x2191-6586
505 0 _aMotivation and Overview -- Learning Based on Similarity -- Part I: Knowledge Transfer from Text Mining to Computer Vision -- State of the Art Approaches for Image Classification -- Local Displacement Estimation of Image Patches and Textons -- Object Recognition with the Bag of Visual Words Model -- Part II: Knowledge Transfer from Computer Vision to Text Mining -- State of the Art Approaches for String and Text Analysis -- Local Rank Distance -- Native Language Identification with String Kernels -- Spatial Information in Text Categorization -- Conclusions.
520 _aThis ground-breaking text/reference diverges from the traditional view that computer vision (for image analysis) and string processing (for text mining) are separate and unrelated fields of study, propounding that images and text can be treated in a similar manner for the purposes of information retrieval, extraction and classification. Highlighting the benefits of knowledge transfer between the two disciplines, the text presents a range of novel similarity-based learning techniques founded on this approach. Topics and features: Describes a variety of similarity-based learning approaches, including nearest neighbor models, local learning, kernel methods, and clustering algorithms Presents a nearest neighbor model based on a novel dissimilarity for images, and applies this for handwritten digit recognition and texture analysis Discusses a novel kernel for (visual) word histograms, as well as several kernels based on pyramid representation, and uses these for facial expression recognition and text categorization by topic Introduces an approach based on string kernels for native language identification Contains links for downloading relevant open source code With a foreword by Prof. Florentina Hristea This unique work will be of great benefit to researchers, postgraduate and advanced undergraduate students involved in machine learning, data science, text mining and computer vision. Dr. Radu Tudor Ionescu is an Assistant Professor in the Department of Computer Science at the University of Bucharest, Romania. Dr. Marius Popescu is an Associate Professor at the same institution.
650 0 _aComputer science.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aImage processing.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aData Mining and Knowledge Discovery.
700 1 _aPopescu, Marius.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319303659
830 0 _aAdvances in Computer Vision and Pattern Recognition,
_x2191-6586
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-30367-3
912 _aZDB-2-SCS
942 _cEBK
999 _c59096
_d59096