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access icon free New unsupervised hybrid classifier based on the fuzzy integral: applied to natural textured images

© The Institution of Engineering and Technology
Received 11/10/2011, Accepted 18/12/2012, Revised 11/10/2012, Published

This study presents a new unsupervised hybrid classifier for natural texture identification in aerial images. The proposed strategy combines through the fuzzy integral (FI) six well-tested base supervised classifiers. This automation is based on the generation of a general rule inferred through decision tree learning, ID3 strategy from the training data. This rule allows generation of a partition of the set of images that the base classifiers use to estimate automatically their parameters. These parameters are the inputs to calculate the relative importance of each classifier in their combination by the FI. The resulting classifier has been compared with related techniques getting an improvement of 8.04% average. The study includes discussion on this comparison.

Inspec keywords: decision trees; unsupervised learning; fuzzy set theory; image classification; natural scenes; image texture

Other keywords: general rule; base supervised classifier; ID3 strategy; natural textured image identification; unsupervised hybrid classifier; fuzzy integral; FI; aerial image; decision tree learning; automatic parameter estimation

Subjects: Computer vision and image processing techniques; Combinatorial mathematics; Combinatorial mathematics; Optical, image and video signal processing; Knowledge engineering techniques

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