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Image Classification by Histogram Features Created with Learning Vector Quantization

  • Marcin Blachnik
  • Jorma Laaksonen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5163)

Abstract

Histogram-based data analysis is one of the most popular solutions for many problems related to image processing such as object recognition and classification. In general a histogram preserves more information from the first-order statistics of the original data than simple averaging of the raw data values. In the simplest case, a histogram model can be specified for a specific image feature type independently of any real image content. In the opposite extreme, the histograms can be made dependent not only the actual image contents, but also on the known semantic classes of the images. In this paper, we propose to use the Learning Vector Quantization (LVQ) algorithm in fine-tuning the codebook vectors for more efficient histogram creation. The performed experiments show that the accuracy of the Interest Point Local Descriptors (IPLD) feature for image classification can be improved by the proposed technique.

Keywords

Support Vector Machine Receiver Operator Characteristic Curve Area Under Curve Interest Point Learn Vector Quantization 
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 2008

Authors and Affiliations

  • Marcin Blachnik
    • 1
    • 2
  • Jorma Laaksonen
    • 2
  1. 1.Electrotechnology DepartmentSilesian University of TechnologyPoland
  2. 2.Adaptive Informatics Research CentreHelsinki University of TechnologyTKKFinland

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