Optimal texture feature selection for the co-occurrence map (original) (raw)

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Abstract

Textures can be described by multidimensional co-occurrence histograms of several pixel gray levels and then classified, e.g., with nearest-neighbors rules. In this work, multidimensional histograms were reduced to two dimensions using the Tree-Structured Self-Organizing Map, here called the Co-occurrence Map. The best components of the co-occurrence vectors, i.e., the spatial displacements minimizing the classification error were selected by exhaustive search. The fast search in the tree-structured maps made it possible to train about 14 000 maps during the feature selection. The highest classification accuracies were obtained using variance-equalized principal components of the co-occurrence vectors. Texture classification with our reduced multidimensional histograms was compared with classification using either channel histograms or standard co-occurrence matrices, which were also selected to minimize the classification error. In all comparisons, the multidimensional histograms performed better than the two other methods.

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Laboratory of Computer and Information Science, Helsinki University of Technology, Rakentajanaukio 2 C, FIN-02150, Espoo, Finland
Kimmo Valkealahti & Erkki Oja

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Kimmo Valkealahti
Erkki Oja

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Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

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Valkealahti, K., Oja, E. (1996). Optimal texture feature selection for the co-occurrence map. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5\_44

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DOI: https://doi.org/10.1007/3-540-61510-5\_44
Published: 09 June 2005
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61510-1
Online ISBN: 978-3-540-68684-2
eBook Packages: Springer Book Archive

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