Local Binary Pattern Based on Magnitude Ranking for Texture Classification

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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New local binary pattern approaches based on color channels in texture classification

Multimedia Tools and Applications ◽

10.1007/s11042-020-09698-5 ◽

2020 ◽

Vol 79 (43-44) ◽

pp. 32541-32561

Author(s):

Ramazan Tekin ◽

Ömer Faruk Ertuğrul ◽

Yılmaz Kaya

Keyword(s):

Local Binary Pattern ◽

Texture Classification ◽

Color Channels

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Bleeding Detection in Gastrointestinal Images using Texture Classification and Local Binary Pattern Technique: A Review

E3S Web of Conferences ◽

10.1051/e3sconf/202017003007 ◽

2020 ◽

Vol 170 ◽

pp. 03007

Author(s):

Aparna Goyal ◽

Reena Gunjan

Keyword(s):

Local Binary Pattern ◽

Texture Classification ◽

Local Binary Patterns ◽

Image Texture ◽

Discriminative Power ◽

Computer Image Analysis ◽

Bleeding Detection ◽

Segmentation Of Images ◽

Effective Description ◽

Lacunarity Analysis

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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