Generic Object Class Detection Using Feature Maps

  • Oscar Danielsson
  • Stefan Carlsson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)


In this paper we describe an object class model and a detection scheme based on feature maps, i.e. binary images indicating occurrences of various local features. Any type of local feature and any number of features can be used to generate feature maps. The choice of which features to use can thus be adapted to the task at hand, without changing the general framework. An object class is represented by a boosted decision tree classifier (which may be cascaded) based on normalized distances to feature occurrences. The resulting object class model is essentially a linear combination of a set of flexible configurations of the features used. Within this framework we present an efficient detection scheme that uses a hierarchical search strategy. We demonstrate experimentally that this detection scheme yields a significant speedup compared to sliding window search. We evaluate the detection performance on a standard dataset [7], showing state of the art results. Features used in this paper include edges, corners, blobs and interest points.


detector AdaBoost decision tree distance transform SIFT 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Oscar Danielsson
    • 1
  • Stefan Carlsson
    • 1
  1. 1.CVAP/CSC, KTHStockholmSweden

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