Impulse-Noise Resistant Color-Texture Classification Approach Using Hybrid Color Local Binary Patterns and Kullback–Leibler Divergence

2017 ◽  
Vol 60 (11) ◽  
pp. 1633-1648 ◽  
Author(s):  
Shervan Fekri-Ershad ◽  
Farshad Tajeripour
Keyword(s):  
Impulse Noise ◽  
Texture Classification ◽  
Local Binary Patterns ◽  
Classification Approach ◽  
Leibler Divergence ◽  
Color Texture

Neighborhood Estimated Local Binary Patterns for Texture Classification

2014 ◽  
Vol 513-517 ◽  
pp. 4401-4406 ◽  
Author(s):  
Ke Chen Song ◽  
Yun Hui Yan
Keyword(s):  
Gaussian Noise ◽  
Texture Classification ◽  
Local Binary Patterns ◽  
Classification Approach ◽  
Local Surrounding

A novel texture classification approach based on neighborhood estimated local binary patterns (NELBP) is proposed. In the proposed approach, the local surrounding values of neighborhood estimated are introduced to operate binary patterns. Moreover, two different and complementary descriptors (average-based descriptor and differences-based descriptor) are extracted from local patches. Contrast experiments on Outex database and CUReT database demonstrate that the proposed NELBP is more robust to Gaussian noise than the conventional LBP for texture classification. In addition, the results also show that the combined complementary descriptor playes an important role in texture classification.

scholarly journals Color texture classification based on proposed impulse-noise resistant color local binary patterns and significant points selection algorithm

Sensor Review ◽  
2017 ◽  
Vol 37 (1) ◽  
pp. 33-42 ◽  
Author(s):  
Shervan Fekriershad ◽  
Farshad Tajeripour
Keyword(s):  
Computational Complexity ◽  
Texture Classification ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Low Noise ◽  
Selection Algorithm ◽  
Content Type ◽  
Color Sensor ◽  
Sensor Information ◽  
Color Texture

Purpose The purpose of this paper is to propose a color-texture classification approach which uses color sensor information and texture features jointly. High accuracy, low noise sensitivity and low computational complexity are specified aims for this proposed approach. Design/methodology/approach One of the efficient texture analysis operations is local binary patterns (LBP). The proposed approach includes two steps. First, a noise resistant version of color LBP is proposed to decrease its sensitivity to noise. This step is evaluated based on combination of color sensor information using AND operation. In a second step, a significant points selection algorithm is proposed to select significant LBPs. This phase decreases final computational complexity along with increasing accuracy rate. Findings The proposed approach is evaluated using Vistex, Outex and KTH-TIPS-2a data sets. This approach has been compared with some state-of-the-art methods. It is experimentally demonstrated that the proposed approach achieves the highest accuracy. In two other experiments, results show low noise sensitivity and low computational complexity of the proposed approach in comparison with previous versions of LBP. Rotation invariant, multi-resolution and general usability are other advantages of our proposed approach. Originality/value In the present paper, a new version of LBP is proposed originally, which is called hybrid color local binary patterns (HCLBP). HCLBP can be used in many image processing applications to extract color/texture features jointly. Also, a significant point selection algorithm is proposed for the first time to select key points of images.

scholarly journals Refined Color Texture Classification Using CNN and Local Binary Pattern

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.

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

Sensors ◽  
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.

Color Texture Classification Using Shortest Paths in Graphs

2014 ◽  
Vol 23 (9) ◽  
pp. 3751-3761 ◽  
Author(s):  
Jarbas Joaci de Mesquita Sa Junior ◽  
Paulo Cesar Cortez ◽  
Andre Ricardo Backes
Keyword(s):  
Texture Classification ◽  
Shortest Paths ◽  
Color Texture

scholarly journals Unsupervised Local Binary Pattern Histogram Selection Scores for Color Texture Classification

2018 ◽  
Vol 4 (10) ◽  
pp. 112 ◽  
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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