NDLI: How to exploit large image data in the fields of texture classification: A case study with local binary patterns

Content Provider	IEEE Xplore Digital Library
Author	Gadermayr, M. Uhl, A.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Salzburg, Salzburg, Austria (Gadermayr, M.; Uhl, A.)
Abstract	In the fields of texture classification, the sizes of images significantly vary according to the respective classification scenario. Whereas quite small image patches mostly lead to good classification accuracies, increasing the image size sometimes even has a negative effect. In this work, we focus on derivatives of Local Binary Patterns as these feature extraction methods offer a high discriminative power and efficiency on the one hand an can be effectively analyzed on the other hand. The aim is to get new insight and furthermore to explore strategies which can help to increase the classification performance. We investigate these strategies which exploit the obviously high distinctiveness of small image patches and simultaneously the redundancy available in large image patches. Finally it can be concluded that the traditionally applied strategies for texture classification should be reconsidered in case of sufficiently large image data.
Starting Page	2515
Ending Page	2519
File Size	576659
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351255
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Histograms Feature extraction Biomedical imaging Computer aided software engineering Computers Redundancy
Content Type	Text
Resource Type	Article

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