Unsupervised Local Binary Pattern Histogram Selection Scores for Color Texture Classification

Mariam Kalakech; Alice Porebski; Nicolas Vandenbroucke; Denis Hamad

doi:10.3390/jimaging4100112

Unsupervised Local Binary Pattern Histogram Selection Scores for Color Texture Classification

Journal of Imaging ◽

10.3390/jimaging4100112 ◽

2018 ◽

Vol 4 (10) ◽

pp. 112 ◽

Cited By ~ 4

Author(s):

Mariam Kalakech ◽

Alice Porebski ◽

Nicolas Vandenbroucke ◽

Denis Hamad

Keyword(s):

Local Binary Pattern ◽

Color Image ◽

Texture Classification ◽

Classification Problems ◽

Class Label ◽

Laplacian Score ◽

Pattern Histogram ◽

Color Texture

These last few years, several supervised scores have been proposed in the literature to select histograms. Applied to color texture classification problems, these scores have improved the accuracy by selecting the most discriminant histograms among a set of available ones computed from a color image. In this paper, two new scores are proposed to select histograms: The adapted Variance score and the adapted Laplacian score. These new scores are computed without considering the class label of the images, contrary to what is done until now. Experiments, achieved on OuTex, USPTex, and BarkTex sets, show that these unsupervised scores give as good results as the supervised ones for LBP histogram selection.

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Combination of LBP Bin and Histogram Selections for Color Texture Classification

Journal of Imaging ◽

10.3390/jimaging6060053 ◽

2020 ◽

Vol 6 (6) ◽

pp. 53

Author(s):

Alice Porebski ◽

Vinh Truong Hoang ◽

Nicolas Vandenbroucke ◽

Denis Hamad

Keyword(s):

Local Binary Pattern ◽

Texture Classification ◽

Selection Procedure ◽

Texture Features ◽

Feature Space ◽

Selection Methods ◽

Texture Descriptor ◽

Classification Problems ◽

The Past ◽

Color Texture

LBP (Local Binary Pattern) is a very popular texture descriptor largely used in computer vision. In most applications, LBP histograms are exploited as texture features leading to a high dimensional feature space, especially for color texture classification problems. In the past few years, different solutions were proposed to reduce the dimension of the feature space based on the LBP histogram. Most of these approaches apply feature selection methods in order to find the most discriminative bins. Recently another strategy proposed selecting the most discriminant LBP histograms in their entirety. This paper tends to improve on these previous approaches, and presents a combination of LBP bin and histogram selections, where a histogram ranking method is applied before processing a bin selection procedure. The proposed approach is evaluated on five benchmark image databases and the obtained results show the effectiveness of the combination of LBP bin and histogram selections which outperforms the simple LBP bin and LBP histogram selection approaches when they are applied independently.

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Local Binary Pattern histogram based Texton learning for texture classification

2011 18th IEEE International Conference on Image Processing ◽

10.1109/icip.2011.6116688 ◽

2011 ◽

Cited By ~ 8

Author(s):

Yonggang He ◽

Nong Sang ◽

Rui Huang

Keyword(s):

Local Binary Pattern ◽

Texture Classification ◽

Pattern Histogram

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Texture Classification Based on Empirical Wavelet Transform Using LBP Features

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v8.i3.pp623-626 ◽

2017 ◽

Vol 8 (3) ◽

pp. 623

Author(s):

Ramesh P. ◽

V. Mathivanan

Keyword(s):

Wavelet Transform ◽

Classification Accuracy ◽

Local Binary Pattern ◽

Color Image ◽

Texture Classification ◽

Automatic Inspection ◽

Gray Scale ◽

Novel Approach ◽

Empirical Wavelet Transform ◽

Inspection Systems

<p>Automatic inspection systems become more importance for industries with high productive plans especially in texture industry. A novel approach to Local Binary Pattern (LBP) feature for texture classification is proposed in this system. At the first, the proposed Empirical Wavelet Transform (EWT) based texture classification is tested on gray scale and color images by using Brodatz texture images. The gray scale and color image is decomposed by EWT at 2 and 3 level of decomposition. LBP features are calculated for each empirical transformed image. Extracted features are given as input to the classification stage. K-NN classifier is used for classification stage. The result of the proposed system gives satisfactory classification accuracy of over 98% for all types of images.</p>

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Refined Color Texture Classification Using CNN and Local Binary Pattern

Mathematical Problems in Engineering ◽

10.1155/2021/5567489 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Khalid M. Hosny ◽

Taher Magdy ◽

Nabil A. Lashin ◽

Kyriakos Apostolidis ◽

George A. Papakostas

Keyword(s):

Color Image ◽

Texture Classification ◽

Local Binary Patterns ◽

Global Features ◽

Key Points ◽

Illumination Changes ◽

Entire Object ◽

New Feature ◽

Almost All ◽

Color Texture

Representation and classification of color texture generate considerable interest within the field of computer vision. Texture classification is a difficult task that assigns unlabeled images or textures to the correct labeled class. Some key factors such as scaling and viewpoint variations and illumination changes make this task challenging. In this paper, we present a new feature extraction technique for color texture classification and recognition. The presented approach aggregates the features extracted from local binary patterns (LBP) and convolution neural network (CNN) to provide discriminatory information, leading to better texture classification results. Almost all of the CNN model cases classify images based on global features that describe the image as a whole to generalize the entire object. LBP classifies images based on local features that describe the image’s key points (image patches). Our analysis shows that using LBP improves the classification task when compared to using CNN only. We test the proposed approach experimentally over three challenging color image datasets (ALOT, CBT, and Outex). The results demonstrated that our approach improved up to 25% in the classification accuracy over the traditional CNN models. We identify optimal combinations for each dataset and obtain high classification rates. The proposed approach is robust, stable, and discriminatory among the three datasets and has shown enhancement in classification and recognition compared to the state-of-the-art method.

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A lazy feature selection method for multi-label classification

Intelligent Data Analysis ◽

10.3233/ida-194878 ◽

2021 ◽

Vol 25 (1) ◽

pp. 21-34

Author(s):

Rafael B. Pereira ◽

Alexandre Plastino ◽

Bianca Zadrozny ◽

Luiz H.C. Merschmann

Keyword(s):

Feature Selection ◽

Text Categorization ◽

Feature Selection Method ◽

Selection Method ◽

Video Classification ◽

Classification Problems ◽

Class Label ◽

New Feature ◽

Feature Selection Techniques ◽

Biomolecular Analysis

In many important application domains, such as text categorization, biomolecular analysis, scene or video classification and medical diagnosis, instances are naturally associated with more than one class label, giving rise to multi-label classification problems. This has led, in recent years, to a substantial amount of research in multi-label classification. More specifically, feature selection methods have been developed to allow the identification of relevant and informative features for multi-label classification. This work presents a new feature selection method based on the lazy feature selection paradigm and specific for the multi-label context. Experimental results show that the proposed technique is competitive when compared to multi-label feature selection techniques currently used in the literature, and is clearly more scalable, in a scenario where there is an increasing amount of data.

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T1K+: A Database for Benchmarking Color Texture Classification and Retrieval Methods

Sensors ◽

10.3390/s21031010 ◽

2021 ◽

Vol 21 (3) ◽

pp. 1010

Author(s):

Claudio Cusano ◽

Paolo Napoletano ◽

Raimondo Schettini

Keyword(s):

Design Of Experiments ◽

Texture Classification ◽

Construction Materials ◽

Heterogeneous Database ◽

High Quality ◽

Visual Descriptors ◽

Classification Tasks ◽

Color Texture

In this paper we present T1K+, a very large, heterogeneous database of high-quality texture images acquired under variable conditions. T1K+ contains 1129 classes of textures ranging from natural subjects to food, textile samples, construction materials, etc. T1K+ allows the design of experiments especially aimed at understanding the specific issues related to texture classification and retrieval. To help the exploration of the database, all the 1129 classes are hierarchically organized in 5 thematic categories and 266 sub-categories. To complete our study, we present an evaluation of hand-crafted and learned visual descriptors in supervised texture classification tasks.

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