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Efficient Object Category Recognition Using Classemes

  • Lorenzo Torresani
  • Martin Szummer
  • Andrew Fitzgibbon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6311)

Abstract

We introduce a new descriptor for images which allows the construction of efficient and compact classifiers with good accuracy on object category recognition. The descriptor is the output of a large number of weakly trained object category classifiers on the image. The trained categories are selected from an ontology of visual concepts, but the intention is not to encode an explicit decomposition of the scene. Rather, we accept that existing object category classifiers often encode not the category per se but ancillary image characteristics; and that these ancillary characteristics can combine to represent visual classes unrelated to the constituent categories’ semantic meanings.

The advantage of this descriptor is that it allows object-category queries to be made against image databases using efficient classifiers (efficient at test time) such as linear support vector machines, and allows these queries to be for novel categories. Even when the representation is reduced to 200 bytes per image, classification accuracy on object category recognition is comparable with the state of the art (36% versus 42%), but at orders of magnitude lower computational cost.

Keywords

Training Image Category Label Image Search Linear Support Vector Machine Multiple Kernel Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Lorenzo Torresani
    • 1
  • Martin Szummer
    • 2
  • Andrew Fitzgibbon
    • 2
  1. 1.Darthmouth CollegeHanoverUSA
  2. 2.Microsoft ResearchCambridgeUnited Kingdom

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