Optimizing Dissimilarity-Based Classifiers Using a Newly Modified Hausdorff Distance

  • Sang-Woon Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4303)


The aim of this paper is to present a dissimilarity measure strategy by which a new philosophy for pattern classification that pertaining to Dissimilarity-Based Classifiers (DBCs) can be efficiently implemented. DBCs, proposed by Duin and his co-authors, is not based on the feature measurements of the individual patterns, but rather on a suitable dissimilarity measure between them. The advantage of DBCs is that since it does not operate on the class-conditional distributions, the accuracy can exceed the Bayes’ error bound. The problem with this strategy, however, is that we need to measure the inter-pattern dissimilarities for all the training samples such that there is no zero distance between objects of different classes. Consequently, the classes do not overlap, and therefore, the lower error bound is zero. Thus, to achieve the desired classification accuracy, a suitable method of measuring dissimilarities is required to overcome the limitations based on the object variations. In this paper, to optimize DBCs, we suggest a newly modified Hausdorff distance measure, which determines the distance directly from the input gray-level image without extracting the binary edge image from it. Also, instead of obtaining the Hausdorff distance on the basis of the entire image, we advocate the use of a spatially weighted mask, which divides the entire image region into several subregions according to their importance. For instance, in face recognition, important regions could include eyes and mouth, while the rest is considered unimportant regions. There could also be the background region that contains no facial parts. The present experimental results, which, to the best of the authors’ knowledge, are the first reported results, demonstrate that the proposed mechanism could increase the classification accuracy when compared with the “conventional” approaches for a well-known face database.


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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sang-Woon Kim
    • 1
  1. 1.Senior Member IEEE, Dept. of Computer Science and EngineeringMyongji UniversityYonginKorea

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