Refined Color Texture Classification Using CNN and Local Binary Pattern

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

The Computer Journal ◽

10.1093/comjnl/bxx033 ◽

2017 ◽

Vol 60 (11) ◽

pp. 1633-1648 ◽

Cited By ~ 8

Author(s):

Shervan Fekri-Ershad ◽

Farshad Tajeripour

Keyword(s):

Impulse Noise ◽

Texture Classification ◽

Local Binary Patterns ◽

Classification Approach ◽

Leibler Divergence ◽

Color Texture

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

Sensor Review ◽

10.1108/sr-07-2016-0120 ◽

2017 ◽

Vol 37 (1) ◽

pp. 33-42 ◽

Cited By ~ 11

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.

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Improved opponent color local binary patterns: an effective local image descriptor for color texture classification

Journal of Electronic Imaging ◽

10.1117/1.jei.27.1.011002 ◽

2017 ◽

Vol 27 (01) ◽

pp. 1 ◽

Cited By ~ 17

Author(s):

Francesco Bianconi ◽

Raquel Bello-Cerezo ◽

Paolo Napoletano

Keyword(s):

Texture Classification ◽

Local Binary Patterns ◽

Image Descriptor ◽

Opponent Color ◽

Local Image Descriptor ◽

Local Image ◽

Color Texture

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CFA local binary patterns for fast illuminant-invariant color texture classification

Journal of Real-Time Image Processing ◽

10.1007/s11554-012-0302-5 ◽

2012 ◽

Vol 10 (2) ◽

pp. 387-401 ◽

Cited By ~ 6

Author(s):

Olivier Losson ◽

Ludovic Macaire

Keyword(s):

Texture Classification ◽

Local Binary Patterns ◽

Illuminant Invariant ◽

Color Texture

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Early Detection of High Blood Pressure and Diabetic Retinopathy on Retinal Fundus Images Using CBRIR Based on Lifting Wavelets

International Journal of Advances in Applied Sciences ◽

10.11591/ijaas.v7.i4.pp334-346 ◽

2018 ◽

Vol 7 (4) ◽

pp. 334

Author(s):

S. S. Tadasare ◽

S. S. Pawar

Keyword(s):

Simulation Analysis ◽

Color Image ◽

Texture Features ◽

Fundus Images ◽

Global Features ◽

Retrieval Accuracy ◽

Lifting Wavelet ◽

Retinal Fundus Images ◽

Almost All ◽

Retinal Fundus

In this paper we present a lifting wavelet based CBRIR image retrieval system that uses color and texture as visual features to describe the content of a retinal fundus images. Our contribution is of three directions. First, we use lifting wavelets 9/7 for lossy and SPL5/3 for lossless to extract texture features from arbitrary shaped retinal fundus regions separated from an image to increase the system effectiveness. This process is performed offline before query processing, therefore to answer a query our system does not need to search the entire database images; instead just a number of similar class type patient images are required to be searched for image similarity. Third, to further increase the retrieval accuracy of our system, we combine the region based features extracted from image regions, with global features extracted from the whole image, which are texture using lifting wavelet and HSV color histograms. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. The experimental evaluation of the system is based on a db1 online retinal fundus color image database. From the experimental results, it is evident that our system performs significantly better accuracy as compared with traditional wavelet based systems. In our simulation analysis, we provide a comparison between retrieval results based on features extracted from the whole image using lossless 5/3 lifting wavelet and features extracted using lossless 9/7 lifting wavelet and using traditional wavelet. The results demonstrate that each type of feature is effective for a particular type of disease of retinal fundus images according to its semantic contents, and using lossless 5/3 lifting wavelet of them gives better retrieval results for almost all semantic classes and outperform 4-10% more accuracy than traditional wavelet.

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Auto-Colorization of Historical Images Using Deep Convolutional Neural Networks

Mathematics ◽

10.3390/math8122258 ◽

2020 ◽

Vol 8 (12) ◽

pp. 2258

Author(s):

Madhab Raj Joshi ◽

Lewis Nkenyereye ◽

Gyanendra Prasad Joshi ◽

S. M. Riazul Islam ◽

Mohammad Abdullah-Al-Wadud ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

User Study ◽

Mean Squared Error ◽

Color Image ◽

Machine Learning Techniques ◽

Global Features ◽

Black And White ◽

Historical Images ◽

Learning Techniques

Enhancement of Cultural Heritage such as historical images is very crucial to safeguard the diversity of cultures. Automated colorization of black and white images has been subject to extensive research through computer vision and machine learning techniques. Our research addresses the problem of generating a plausible colored photograph of ancient, historically black, and white images of Nepal using deep learning techniques without direct human intervention. Motivated by the recent success of deep learning techniques in image processing, a feed-forward, deep Convolutional Neural Network (CNN) in combination with Inception- ResnetV2 is being trained by sets of sample images using back-propagation to recognize the pattern in RGB and grayscale values. The trained neural network is then used to predict two a* and b* chroma channels given grayscale, L channel of test images. CNN vividly colorizes images with the help of the fusion layer accounting for local features as well as global features. Two objective functions, namely, Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), are employed for objective quality assessment between the estimated color image and its ground truth. The model is trained on the dataset created by ourselves with 1.2 K historical images comprised of old and ancient photographs of Nepal, each having 256 × 256 resolution. The loss i.e., MSE, PSNR, and accuracy of the model are found to be 6.08%, 34.65 dB, and 75.23%, respectively. Other than presenting the training results, the public acceptance or subjective validation of the generated images is assessed by means of a user study where the model shows 41.71% of naturalness while evaluating colorization results.

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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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