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008 140325s2014 gw | s |||| 0|eng d
020 _a9783319056968
_9978-3-319-05696-8
024 7 _a10.1007/978-3-319-05696-8
_2doi
050 4 _aTA1637-1638
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aUYQV
_2bicssc
072 7 _aCOM012000
_2bisacsh
072 7 _aCOM016000
_2bisacsh
082 0 4 _a006.6
_223
082 0 4 _a006.37
_223
245 1 0 _aFusion in Computer Vision
_h[electronic resource] :
_bUnderstanding Complex Visual Content /
_cedited by Bogdan Ionescu, Jenny Benois-Pineau, Tomas Piatrik, Georges Qu�enot.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXIV, 272 p. 74 illus., 65 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 _aA Selective Weighted Late Fusion for Visual Concept Recognition -- Bag-of-Words Image Representation: Key Ideas and Further Insight -- Hierarchical Late Fusion for Concept Detection in Videos -- Fusion of Multiple Visual Cues for Object Recognition in Video -- Evaluating Multimedia Features and Fusion for Example-Based Event Detection -- Rotation-Based Ensemble Classifiers for High Dimensional Data -- Multimodal Fusion in Surveillance Applications -- Multimodal Violence Detection in Hollywood Movies: State-of-the-Art and Benchmarking -- Fusion Techniques in Biomedical Information Retrieval -- Using Crowdsourcing to Capture Complexity in Human Interpretations of Multimedia Content.
520 _aVisual content understanding is a complex and important challenge for applications in automatic multimedia information indexing, medicine, robotics, and surveillance. Yet the performance of such systems can be improved by the fusion of individual modalities/techniques for content representation and machine learning. This comprehensive text/reference presents a thorough overview of Fusion in Computer Vision, from an interdisciplinary and multi-application viewpoint. Presenting contributions from an international selection of experts, the work describes numerous successful approaches, evaluated in the context of international benchmarks that model realistic use cases at significant scales. Topics and features: Examines late fusion approaches for concept recognition in images and videos, including the bag-of-words model Describes the interpretation of visual content by incorporating models of the human visual system with content understanding methods Investigates the fusion of multi-modal features of different semantic levels, as well as results of semantic concept detections, for example-based event recognition in video Proposes rotation-based ensemble classifiers for high-dimensional data, which encourage both individual accuracy and diversity within the ensemble Reviews application-focused strategies of fusion in video surveillance, biomedical information retrieval, and content detection in movies Discusses the modeling of mechanisms of human interpretation of complex visual content This authoritative collection is essential reading for researchers and students interested in the domain of information fusion for complex visual content understanding, and related fields.
650 0 _aComputer science.
650 0 _aData mining.
650 0 _aMultimedia information systems.
650 0 _aArtificial intelligence.
650 0 _aImage processing.
650 1 4 _aComputer Science.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aMultimedia Information Systems.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aData Mining and Knowledge Discovery.
700 1 _aIonescu, Bogdan.
_eeditor.
700 1 _aBenois-Pineau, Jenny.
_eeditor.
700 1 _aPiatrik, Tomas.
_eeditor.
700 1 _aQu�enot, Georges.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319056951
830 0 _aAdvances in Computer Vision and Pattern Recognition,
_x2191-6586
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-05696-8
912 _aZDB-2-SCS
942 _cEBK
999 _c57057
_d57057