Learning and recognizing texture characteristics using local binary patterns
1University of Oulu, Faculty of Technology, Department of Electrical and Information Engineering
2University of Oulu, Infotech Oulu
|Online Access:||PDF Full Text (PDF, 4.1 MB)|
|Persistent link:|| http://urn.fi/urn:isbn:9789514285028
|Publish Date:|| 2007-06-05
|Thesis type:||Doctoral Dissertation
|Defence Note:||Academic dissertation to be presented, with the assent of the Faculty of Technology of the University of Oulu, for public defence in Auditorium TS101, Linnanmaa, on June 15th, 2007, at 12 noon
Doctor Jorma Laaksonen
Doctor Lasse Lensu
Texture plays an important role in numerous computer vision applications. Many methods for describing and analyzing of textured surfaces have been proposed. Variations in the appearance of texture caused by changing illumination and imaging conditions, for example, set high requirements on different analysis methods. In addition, real-world applications tend to produce a great deal of complex texture data to be processed that should be handled effectively in order to be exploited.
A local binary pattern (LBP) operator offers an efficient way of analyzing textures. It has a simple theory and combines properties of structural and statistical texture analysis methods. LBP is invariant against monotonic gray-scale variations and has also extensions to rotation invariant texture analysis.
Analysis of real-world texture data is typically very laborious and time consuming. Often there is no ground truth or other prior knowledge of the data available, and important properties of the textures must be learned from the images. This is a very challenging task in texture analysis.
In this thesis, methods for learning and recognizing texture categories using local binary pattern features are proposed. Unsupervised clustering and dimensionality reduction methods combined to visualization provide useful tools for analyzing texture data. Uncovering the data structures is done in an unsupervised fashion, based only on texture features, and no prior knowledge of the data, for example texture classes, is required. In this thesis, non-linear dimensionality reduction, data clustering and visualization are used for building a labeled training set for a classifier, and for studying the performance of the features.
The thesis also proposes a multi-class approach to learning and labeling part based texture appearance models to be used in scene texture recognition using only little human interaction. Also a semiautomatic approach to learning texture appearance models for view based texture classification is proposed.
The goal of texture characterization is often to classify textures into different categories. In this thesis, two texture classification systems suitable for different applications are proposed. First, a discriminative classifier that combines local and contextual texture information of the image in scene recognition is proposed. Secondly, a real-time capable texture classifier with a self-intuitive user interface to be used in industrial texture classification is proposed.
Two challenging real-world texture analysis applications are used to study the performance and usefulness of the proposed methods. The first one is visual paper analysis which aims to characterize paper quality based on texture properties. The second application is outdoor scene image analysis where texture information is used to recognize different regions in the scenes.
Acta Universitatis Ouluensis. C, Technica
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