Image Texture Classification Using Texture Spectrum and Local Binary Pattern

Texture analysis has proven to be a breakthrough in many applications of computer image analysis. It has been used for classification or segmentation of images which requires an effective description of image texture. Due to high discriminative power and simplicity of computation, the local binary pattern descriptors have been used for distinguishing different textures and in extracting texture and color in medical images. This paper discusses performance of various texture classification techniques using Contourlet Transform, Discrete Fourier Transform, Local Binary Patterns and Lacunarity analysis. The study reveals that the incorporation of efficient image segmentation, enhancement and texture classification using local binary pattern descriptor detects bleeding region in human intestines precisely.

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CNN performance dependence on linear image processing

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-182 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 310-1-310-7

Author(s):

Khalid Omer ◽

Luca Caucci ◽

Meredith Kupinski

Keyword(s):

Image Processing ◽

Texture Classification ◽

Full Rank ◽

Detection Performance ◽

Ideal Observer ◽

Training Data ◽

Image Texture ◽

Training Images ◽

Analytic Expressions ◽

Linear Compression

This work reports on convolutional neural network (CNN) performance on an image texture classification task as a function of linear image processing and number of training images. Detection performance of single and multi-layer CNNs (sCNN/mCNN) are compared to optimal observers. Performance is quantified by the area under the receiver operating characteristic (ROC) curve, also known as the AUC. For perfect detection AUC = 1.0 and AUC = 0.5 for guessing. The Ideal Observer (IO) maximizes AUC but is prohibitive in practice because it depends on high-dimensional image likelihoods. The IO performance is invariant to any fullrank, invertible linear image processing. This work demonstrates the existence of full-rank, invertible linear transforms that can degrade both sCNN and mCNN even in the limit of large quantities of training data. A subsequent invertible linear transform changes the images’ correlation structure again and can improve this AUC. Stationary textures sampled from zero mean and unequal covariance Gaussian distributions allow closed-form analytic expressions for the IO and optimal linear compression. Linear compression is a mitigation technique for high-dimension low sample size (HDLSS) applications. By definition, compression strictly decreases or maintains IO detection performance. For small quantities of training data, linear image compression prior to the sCNN architecture can increase AUC from 0.56 to 0.93. Results indicate an optimal compression ratio for CNN based on task difficulty, compression method, and number of training images.

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SVM-PSO based rotation-invariant image texture classification in SVD and DWT domains

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2016.02.005 ◽

2016 ◽

Vol 52 ◽

pp. 96-107 ◽

Cited By ~ 10

Author(s):

Bae-Muu Chang ◽

Hung-Hsu Tsai ◽

Chih-Yuan Yen

Keyword(s):

Texture Classification ◽

Image Texture ◽

Rotation Invariant

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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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Local Derivative Vector Pattern: Hybrid Pattern for Content-Based Medical Image Retrieval

Review of Computer Engineer Studies ◽

10.18280/rces.070401 ◽

2020 ◽

Vol 7 (4) ◽

pp. 79-86

Author(s):

Nagadevi Darapureddy ◽

Nagaprakash Karatapu ◽

Tirumala Krishna Battula

Keyword(s):

Image Retrieval ◽

Texture Analysis ◽

Medical Image ◽

Local Binary Pattern ◽

Medical Images ◽

Texture Classification ◽

Medical Image Retrieval ◽

Local Vector ◽

Local Derivative ◽

Local Derivative Pattern

This paper examines a hybrid pattern i.e. Local derivative Vector pattern and comparasion of this pattern over other different patterns for content-based medical image retrieval. In recent years Pattern-based texture analysis has significant popularity for a variety of tasks like image recognition, image and texture classification, and object detection, etc. In literature, different patterns exist for texture analysis. This paper aims at forming a hybrid pattern compared in terms of precision, recall and F1-score with different patterns like Local Binary Pattern (LBP), Local Derivative Pattern (LDP), Completed Local Binary Pattern (CLBP), Local Tetra Pattern (LTrP), Local Vector Pattern (LVP) and Local Anisotropic Pattern (LAP) which were applied on medical images for image retrieval. The proposed method is evaluated on different modalities of medical images. The results of the proposed hybrid pattern show biased performance compared to the state-of-the-art. So this can further extended with other pattern to form a hybrid pattern.

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Radial mean local binary pattern for noisy texture classification

Multimedia Tools and Applications ◽

10.1007/s11042-017-5440-0 ◽

2018 ◽

Vol 77 (16) ◽

pp. 21481-21508 ◽

Cited By ~ 5

Author(s):

Mohammad Hossein Shakoor ◽

Reza Boostani

Keyword(s):

Local Binary Pattern ◽

Texture Classification

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HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

Sensors ◽

10.3390/s18072045 ◽

2018 ◽

Vol 18 (7) ◽

pp. 2045 ◽

Cited By ~ 11

Author(s):

Haris Khan ◽

Sofiane Mihoubi ◽

Benjamin Mathon ◽

Jean-Baptiste Thomas ◽

Jon Hardeberg

Keyword(s):

Spectral Analysis ◽

High Resolution ◽

Matrix Factorization ◽

Local Binary Pattern ◽

Near Infrared ◽

Texture Classification ◽

Hyperspectral Images ◽

Close Range ◽

Local Binary Pattern Feature ◽

Non Negative Matrix Factorization

We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.

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