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Learning graphs to model visual objects across different depictive styles


Reference:

Wu, Q., Cai, H. and Hall, P., 2014. Learning graphs to model visual objects across different depictive styles. In: Fleet, D., Pajdla, T., Schiele, B. and Tuytelaars, T., eds. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).Vol. 8695. Springer, pp. 313-328.

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      Official URL:

      http://dx.doi.org/10.1007/978-3-319-10584-0_21

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      Abstract

      Visual object classification and detection are major problems in contemporary computer vision. State-of-art algorithms allow thousands of visual objects to be learned and recognized, under a wide range of variations including lighting changes, occlusion, point of view and different object instances. Only a small fraction of the literature addresses the problem of variation in depictive styles (photographs, drawings, paintings etc.). This is a challenging gap but the ability to process images of all depictive styles and not just photographs has potential value across many applications. In this paper we model visual classes using a graph with multiple labels on each node; weights on arcs and nodes indicate relative importance (salience) to the object description. Visual class models can be learned from examples from a database that contains photographs, drawings, paintings etc. Experiments show that our representation is able to improve upon Deformable Part Models for detection and Bag of Words models for classification.

      Details

      Item Type Book Sections
      CreatorsWu, Q., Cai, H. and Hall, P.
      EditorsFleet, D., Pajdla, T., Schiele, B. and Tuytelaars, T.
      DOI10.1007/978-3-319-10584-0_21
      Related URLs
      URLURL Type
      http://www.scopus.com/inward/record.url?scp=84906352971&partnerID=8YFLogxKUNSPECIFIED
      http://eccv2014.org/Organisation
      DepartmentsFaculty of Science > Computer Science
      Research CentresMedia Technology Research Centre
      StatusPublished
      ID Code41062

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