Pixel classification using general adaptive neighborhood-based features

Víctor González-Castro, Johan Debayle, Vladimir Ćurić

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


This paper introduces a new descriptor for characterizing and classifying the pixels of texture images by means of General Adaptive Neighborhoods (GANs). The GAN of a pixel is a spatial region surrounding it and fitting its local image structure. The features describing each pixel are then regionbased and intensity-based measurements of its corresponding GAN. In addition, these features are combined with the graylevel values of adaptive mathematical morphology operators using GANs as structuring elements. The classification of each pixel of images belonging to five different textures of the VisTex database has been carried out to test the performance of this descriptor. For the sake of comparison, other adaptive neighborhoods introduced in the literature have also been used to extract these features from: the Morphological Amoebas (MA), adaptive geodesic neighborhoods (AGN) and salience adaptive structuring elements (SASE). Experimental results show that the GAN-based method outperforms the others for the performed classification task, achieving an overall accuracy of 97.25% in the five-way classifications, and area under curve values close to 1 in all the five one class vs. all classes' binary classification problems.'

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
CountryUnited Kingdom


  • Adaptive mathematical morphology
  • Adaptive neighborhoods
  • Minkowski functionals
  • Morphometrical functionals
  • Pixel description


Dive into the research topics of 'Pixel classification using general adaptive neighborhood-based features'. Together they form a unique fingerprint.

Cite this