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Combining Features for Texture Analysis

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Computer Analysis of Images and Patterns (CAIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9257))

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Abstract

In the present paper we consider building feature vectors for texture analysis by combining information provided by two techniques.The first feature extraction method (the Discrete Wavelet Transform) is applied to the entire image. By computing the Gini index for several subimages of a given texture, we choose one that maximizes this measure. For the selected subimage we apply the second technique (a Gabor filter) for feature extraction. When we combine the two vectors, the classification results are better than the one obtained using only one set of features. The classification was performed on the Brodatz album, using a naive Bayes classifier.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science, University “Alexandru Ioan Cuza”, Iaşi, Romania

    Anca Ignat

Authors
  1. Anca Ignat
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Corresponding author

Correspondence to Anca Ignat .

Editor information

Editors and Affiliations

  1. University of Malta, Msida, Malta

    George Azzopardi

  2. University of Groningen, Groningen, The Netherlands

    Nicolai Petkov

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© 2015 Springer International Publishing Switzerland

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Ignat, A. (2015). Combining Features for Texture Analysis. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9257. Springer, Cham. https://doi.org/10.1007/978-3-319-23117-4_19

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  • DOI: https://doi.org/10.1007/978-3-319-23117-4_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23116-7

  • Online ISBN: 978-3-319-23117-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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