Pixel Classification Using General Adaptive Neighborhood-Based Features
2014 (English)In: Proceedings 22nd International Conference on Pattern Recognition (ICPR) 2014, 2014, 3750-3755 p.Conference paper (Refereed)
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 region-based and intensity-based measurements of its corresponding GAN. In addition, these features are combined with the gray-level 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.
Place, publisher, year, edition, pages
2014. 3750-3755 p.
, International Conference on Pattern Recognition, ISSN 1051-4651
IdentifiersURN: urn:nbn:se:uu:diva-238692DOI: 10.1109/ICPR.2014.644ISI: 000359818003148ISBN: 978-1-4799-5208-3OAI: oai:DiVA.org:uu-238692DiVA: diva2:771875
22nd International Conference on Pattern Recognition, Stockholm, Sweden, August 24-28, 2014