An Improved Feature Extraction Method for Texture Classification with Increased Noise Robustness

Barburiceanu, Stefania; Meza, Serban; Germain, Christian; Terebes, Romulus

doi:10.23919/eusipco.2019.8902765

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…Even if the LBP operator is efficient in many texture classification problems, it has a great disadvantage: the noise sensitivity. In order to handle this issue and to increase the discrimination power of the LBP, we proposed in a previous work [22], the Block Matching and 3D Filtering Extended Local Binary Patterns (BM3DELBP) operator which was inspired by the Median Robust Extended Local Binary Patterns formalism [23]. Instead of considering raw pixel values like LBP, BM3DELBP introduces a filtering step in the feature extraction operation and considers responses to a state-of-the-art filter, the Block Matching and 3D Filtering (BM3D) [24].…”

Section: Block Matching and 3d Filtering Extended Local Binary Patter...mentioning

confidence: 99%

“…Two scales were considered given by their radius: R = 2 and R = 4, respectively. In [22], four scales were used for 2D image classification. Since we deal with volumetric images and the computation complexity is challenging, we proposed reducing the number of scales to two in order to minimize the feature vector size and to avoid the classic curse of dimensionality problem.…”

Section: The Extension Of Bm3delbp To 3d (Bm3delbp_3d)mentioning

confidence: 99%

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3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

2021

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Lately, 3D imaging techniques have achieved a lot of progress due to recent developments in 3D sensor technologies. This leads to a great interest regarding 3D image feature extraction and classification techniques. As pointed out in literature, one of the most important and discriminative features in images is the textural content. Within this context, we propose a texture feature extraction technique for volumetric images with improved discrimination power. The method could be used in textured volumetric data classification tasks. To achieve this, we fuse two complementary pieces of information, feature vectors derived from Local Binary Patterns (LBP) and the Gray-Level Co-occurrence Matrix-based methods. They provide information regarding the image pattern and the contrast, homogeneity and local anisotropy in the volumetric data, respectively. The performance of the proposed technique was evaluated on a public dataset consisting of volumetric textured images affected by several transformations. The classifiers used are the Support Vector Machine, k-Nearest Neighbours and Random Forest. Our method outperforms other handcrafted 3D or 2D texture feature extraction methods and typical deep-learning networks. The proposed technique improves the discrimination power and achieves promising results even if the number of images per class is relatively small.

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Section: Block Matching and 3d Filtering Extended Local Binary Patter...mentioning

confidence: 99%

Section: The Extension Of Bm3delbp To 3d (Bm3delbp_3d)mentioning

confidence: 99%

3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

2021

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“…Later, several LBP-derived operators which provide improved invariance to different transformations and a greater discrimination power were proposed, such as the Median Robust Extended Local Binary Patterns (MRELBP) [12]. Also, in order to improve the robustness to Gaussian noise, the Block Matching and 3D Filtering Extended Local Binary Patterns (BM3DELBP) was introduced by us in [13]. Another popular texture feature descriptor is the Gray-Level Cooccurrence Matrix (GLCM) [14] which achieved significant performance for texture classification tasks as reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture

et al. 2021

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This paper studies the use of deep-learning models (AlexNet, VggNet, ResNet) pre-trained on object categories (ImageNet) in applied texture classification problems such as plant disease detection tasks. Research related to precision agriculture is of high relevance due to its potential economic impact on agricultural productivity and quality. Within this context, we propose a deep learning-based feature extraction method for the identification of plant species and the classification of plant leaf diseases. We focus on results relevant to real-time processing scenarios that can be easily transferred to manned/unmanned agricultural smart machinery (e.g. tractors, drones, robots, IoT smart sensor networks, etc.) by reconsidering the common processing pipeline. In our approach, texture features are extracted from different layers of pre-trained Convolutional Neural Network models and are later applied to a machine-learning classifier. For the experimental evaluation, we used publicly available datasets consisting of RGB textured images and datasets containing images of healthy and non-healthy plant leaves of different species. We compared our method to feature vectors derived from traditional handcrafted feature extraction descriptors computed for the same images and end-to-end deep-learning approaches. The proposed method proves to be significantly more efficient in terms of processing times and discriminative power, being able to surpass traditional and end-toend CNN-based methods and provide a solution also to the problem of the reduced datasets available for precision agriculture.

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Grape Leaf Disease Classification using LBP-derived Texture Operators and Colour

Barburiceanu

Terebes

Meza

2020

2020 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR)

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An Improved Feature Extraction Method for Texture Classification with Increased Noise Robustness

Cited by 11 publications

References 13 publications

3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture

Grape Leaf Disease Classification using LBP-derived Texture Operators and Colour

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