Local oriented statistics information booster (LOSIB) for texture classification

Oscar García-Olalla, Enrique Alegre, Laura Fernández-Robles, Victor González-Castro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Local oriented statistical information booster (LOSIB) is a descriptor enhancer based on the extraction of the gray level differences along several orientations. Specifically, the mean of the differences along particular orientations is considered. In this paper we have carried out some experiments using several classical texture descriptors to show that classification results are better when they are combined with LOSIB, than without it. Both parametric and non-parametric classifiers, Support Vector Machine and k-Nearest Neighbourhoods respectively, were applied to assess this new method. Furthermore, two different texture dataset were evaluated: KTH-Tips-2a and Brodatz32 to prove the robustness of LOSIB. Global descriptors such as WCF4 (Wavelet Co-occurrence Features), that extracts Haralick features from the Wavelet Transform, have been combined with LOSIB obtaining an improvement of 16.94% on KTH and 7.55% on Brodatz when classifying with SVM. Moreover, LOSIB was used together with state-of-the-art local descriptors such as LBP (Local Binary Pattern) and several of its recent variants. Combined with CLBP (Complete LBP), the LOSIB booster results were improved in 5.80% on KTH-Tips 2a and 7.09% on the Brodatz dataset. For all the tested descriptors, we have observed that a higher performance has been achieved, with the two classifiers on both datasets, when using some LOSIB settings.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781479952083
Publication statusPublished - 1 Jan 2014
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, United Kingdom
Duration: 24 Aug 201428 Aug 2014


Conference22nd International Conference on Pattern Recognition, ICPR 2014
Country/TerritoryUnited Kingdom

Keywords / Materials (for Non-textual outputs)

  • Booster
  • Descriptor
  • Texture retrieval


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