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Outex - new framework for empirical evaluation of texture analysis algorithms

This paper presents the current status of a new initiative aimed at developing a versatile framework and image database for empirical evaluation of texture analysis algorithms. The proposed Outex framework contains a large collection of surface textures captured under different conditions, which facilitates construction of a wide range of texture analysis problems. The problems are encapsulated into test suites, for which baseline results obtained with algorithms from literature are provided. The rich functionality of the framework is demonstrated with examples in texture classification, segmentation and retrieval. The framework has a web site for public dissemination of the database and comparative results obtained by research groups world wide.

Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns

This paper presents a theoretically very simple yet efficient approach for gray scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The proposed approach is very robust in terms of gray scale variations, since the operators are by definition invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity, as the operators can be realized with a few operations in a small neighborhood and a lookup table. Excellent experimental results obtained in two true problems of rotation invariance, where the classifier is trained at one particular rotation angle and tested with samples from other rotation angles, demonstrate that good discrimination can be achieved with the statistics of simple rotation invariant local binary patterns. These operators characterize the spatial configuration of local image texture and the performance can be further improved by combining them with rotation invariant variance measures that characterize the contrast of local image texture. The joint distributions of these orthogonal measures are shown to be very powerful tools for rotation invariant texture analysis.

Classification with color and texture: jointly or separately?

Abstract Current approaches to color texture analysis can be roughly divided into two categories: methods that process color and texture information separately, and those that consider color and texture a joint phenomenon. In this paper, both approaches are empirically evaluated with a large set of natural color textures. The classification performance of color indexing methods is compared to gray-scale and color texture methods, and to combined color and texture methods, in static and varying illumination conditions. Based on the results, we argue that color and texture are separate phenomena that can, or even should, be treated individually.

A Generalized Local Binary Pattern Operator for Multiresolution Gray Scale and Rotation Invariant Texture Classification

This paper presents generalizations to the gray scale and rotation invariant texture classification method based on local binary patterns that we have recently introduced. We derive a generalized presentation that allows for realizing a gray scale and rotation invariant LBP operator for any quantization of the angular space and for any spatial resolution, and present a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray scale variations, since the operator is by definition invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity, as the operator can be realized with a few operations in a small neighborhood and a lookup table. Excellent experimental results obtained in a true problem of rotation invariance, where the classifier is trained at one particular rotation angle and tested with samples from other rotation angles, demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns. These operators characterize the spatial configuration of local image texture and the performance can be further improved by combining them with rotation invariant variance measures that characterize the contrast of local image texture. The joint distributions of these orthogonal measures are shown to be very powerful tools for rotation invariant texture analysis.

Multi-scale binary patterns for texture analysis

This paper presents two novel ways of extending the local binary pattern (LBP) texture analysis operator to multiple scales. First, large-scale texture patterns are detected by combining exponentially growing circular neighborhoods with Gaussian low-pass filtering. Second, cellular automata are proposed as a way of compactly encoding arbitrarily large circular neighborhoods. The performance of the extensions is evaluated in classifying natural textures from the Outex database.

View-based recognition of real-world textures

Abstract A new method for recognizing 3D textured surfaces is proposed. Textures are modeled with multiple histograms of micro-textons, instead of more macroscopic textons used in earlier studies. The micro-textons are extracted with the recently proposed multiresolution local binary pattern operator. Our approach has many advantages compared to the earlier approaches and provides the leading performance in the classification of Columbia–Utrecht database textures imaged under different viewpoints and illumination directions. It also provides very promising results in the classification of outdoor scene images. An approach for learning appearance models for view-based texture recognition using self-organization of feature distributions is also proposed. The method performs well in experiments. It can be used for quickly selecting model histograms and rejecting outliers, thus providing an efficient tool for vision system training even when the feature data has a large variability.

Optimising Colour and Texture Features for Real-time Visual Inspection

The role of colour descriptors has been demonstrated to be quite remarkable in many visual inspection tasks. In some other tasks, texture measurements are needed because of unevenly coloured or achromatic surfaces. In many applications, colour and texture must be combined to achieve good performance. At the same time, the computational complexity of the methods must be kept as low as possible. In this paper, a methodology for combining and optimising colour and texture features is proposed. Feature sets are optimised using a deterministic and a randomised approach. Results are demonstrated in detecting and recognising defect types on wooden surfaces.

Real-time surface inspection by texture

In this paper a real-time surface inspection method based on texture features is introduced. The proposed approach is based on the Local Binary Pattern (LBP) texture operator and the Self-Organizing Map (SOM). A very fast software implementation of the LBP operator is presented. The SOM is used as a powerful classifier and visualizer. The efficiency of the method is empirically evaluated in two different problems including textures from the Outex database and from a paper inspection problem.

Face recognition based on the appearance of local regions

Recently, we proposed a novel facial representation for face recognition based on the local binary pattern (LBP) features. We obtained excellent results when dividing the face images into several regions from which the LBP features are extracted and concatenated into an enhanced feature vector as a face descriptor. However, it was unclear whether the obtained results were due to the use of local regions (instead of a holistic approach) or to the discriminative power of LBP. In this work, we investigated this issue by adopting and comparing four different texture features when using the appearances of local regions. The experimental results clearly showed and confirmed the validity of using LBP for face description.

Separating color and pattern information for color texture discrimination

The analysis of colored surface textures is a challenging research problem in computer vision. Current approaches to this task can be roughly divided into two categories: methods that process color and texture information separately and those that utilize multispectral texture descriptions. Motivated by recent psychophysical findings, we find the former approach quite auspicious. We propose the use of complementary color and texture measures that are combined on a higher level, and empirically demonstrate the validity of our proposition using a large set of natural color textures.