Extended multi-structure local binary pattern for high-resolution image scene classification

Bian, Xiaoyong; Chen, Chen; Du, Qian; Sheng, Yuxia

doi:10.1109/igarss.2016.7730338

Cited by 9 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The experimental results showed that the performance of the proposed method is superior to PCA. Bian et al [23] have proposed an extension of the LBP, which is referred to as multistructure LBP, for the process of classifying high-resolution images. The proposed method utilizes three coupled descriptors with multistructure sampling to identify complementary features.…”

Section: Current Trends Of Using Lbpmentioning

confidence: 99%

“…Table 1 summarizes all LBP texture analysis related work that has been illustrated in subsequent paragraphs. [16] Extended LBP Structure Image classification Wan et al [18] Average-LBP Averaging Medical image classification Kim et al [19] Adaptive LBP Structure Image classification for handwritten recognition Dey et al [21] LBP and spatial sampling Structure Image segmentation for handwritten recognition Almezoghy et al [22] PCA-LBP Averaging Image classification for palm recognition Bian et al [23] Multistructure LBP Structure High-resolution image classification Jia et al [24] LBP superpixel-level Structure Image classification for hyperspectral images Yuan et al [25] HDLBP for spatial structure Structure Image classification for material recognition Xu et al [26] PCBP Averaging Face recognition Kou et al [27] PCLBP Structure Image texture classification Khaleefah et al [17,20,28] LBP, ULBP Parameter tuning Paper fingerprinting…”

Section: Current Trends Of Using Lbpmentioning

confidence: 99%

See 1 more Smart Citation

Review of local binary pattern operators in image feature extraction

Khaleefah

Mostafa

Nasrudin

2020

IJEECS

View full text Add to dashboard Cite

<span>With the substantial expansion of image information, image processing and computer vision have significant roles in several applications, including image classification, image segmentation, pattern recognition, and image retrieval. An important feature that has been applied in many image applications is texture. Texture is the characteristic of a set of pixels that form an image. Therefore, analyzing texture has a significant impact on segmenting an image or detecting important portions of an image. This paper provides a review on LBP and its modifications. The aim of this review is to show the current trends for using, modifying and adapting LBP in the domain of image processing.</span>

show abstract

Section: Current Trends Of Using Lbpmentioning

confidence: 99%

Section: Current Trends Of Using Lbpmentioning

confidence: 99%

Review of local binary pattern operators in image feature extraction

Khaleefah

Mostafa

Nasrudin

2020

IJEECS

View full text Add to dashboard Cite

show abstract

“…Experimental results showed that the proposed method has superior performance compared to PCA. Bian et al [16] have proposed an extension of LBP which is called multi-structure LBP for the process of classifying high-resolution images. The proposed method utilizes three coupled descriptors with multi-structure sampling in order to identify complementary features.…”

Section: Current Trends Of Using Lbpmentioning

confidence: 99%

“…Table 1 summarizes all the LBP texture analysis related work that has been illustrated in latter paragraphs. [11] extended LBP Structure Image Classification Wan et al [12] Average-LBP Averaging Medical image classification Kim et al [13] adaptive LBP Structure Image classification for handwritten recognition Dey et al [14] LBP and spatial sampling Structure Image segmentation for handwritten recognition Almezoghy et al [15] PCA-LBP Averaging Image Classification for Palm recognition Bian et al [16] multi-structure LBP Structure High-resolution image classification Jia et al [17] LBP superpixel-level Structure Image Classification for hyperspectral images Yuan et al [18] HDLBP for spatial structure Structure Image classification for material recognition Xu et al [19] PCBP Averaging Face recognition Kou et al [20] PCLBP Structure Image texture Classification…”

Section: Current Trends Of Using Lbpmentioning

confidence: 99%

A survey on local binary pattern and gabor filter as texture descriptors of smart profiling systems

Khaleefah

Mostafa

Samsudin

et al. 2020

IJEECS

View full text Add to dashboard Cite

With the dramatic expansion of image information nowadays, image processing and computer visions are playing a significant role in terms of several applications such as image classification, image segmentation, pattern recognition, and image retrieval. One of the important features that have been used in many image applications is texture. The texture is the characteristic of a set of pixels that formed the image. Therefore, analyzing such texture would have a significant impact on segmenting the image or detecting important portions of such image. This paper aims to overview the feature extraction and description techniques depicted in the literature to characterize regions for images. In particular, two of popular descriptors will be examined including Local Binary Pattern (LBP) and Gabor Filter. The key characteristic behind such investigation lies in how the features of an image would contribute toward the process of recognition and image classification. In this regard, an extensive discussion is provided on both LBP and Gabor descriptors along with the efforts that have been intended to combine them. The reason behind investigating these descriptors is that they are considered the most common local and global descriptors used in the literature. The overall aim of this survey is to show current trends on using, modifying and adapting these descriptors in the domain of image processing.

show abstract

“…Özellik çıkarım işlemi, görüntünün pikselleri arasındaki ilişkileri tanımlayabilmek için geliştirilen algoritmaların çıktısıdır. Düşük hafıza tüketimi ve hızlı hesaplama özelliğinden dolayı LBP özellik çıkarım yöntemi mermer, granit, ahşap veya tekstil malzemelerinin sınıflandırılmasında kullanılmaktadır [4][5][6]. Ayrıca LBP özellik çıkarımı kullanılarak aynı türdeki mermerleri seleksiyonlarına ayıran bir çalışma gerçekleştirilmiştir [7].…”

Section: Gi̇ri̇ş (Introduction)unclassified

Grafiksel Arayüz Tabanlı Mermer Sınıflandırma Uygulaması Geliştirme

Turan¹,

Dandil

Bal

2020

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

View full text Add to dashboard Cite

This study focuses on the development of a graphical interface application that automates the marble classification process by removing it from the manual structure. The texture classification process of marbles is a problem that can be overcome by using image processing and machine learning-based technologies together. In the scope of the study, a graphical interface that classifies marbles automatically has been developed. The training set is created by extracting feature extraction of marble images classified with GUI. With classifiers using the training set, marble images of unknown class are classified without the need for expert staff. The main screenshot of the developed GUI is given in Figure A. Figure A. GUI homepagePurpose: Within the scope of the study, it was aimed to develop a system that automates the marble classification process by removing personnel dependency. Histogram, LBP and SIFT were examined in terms of feature extraction and added to the application. As the classification algorithm, Extreme Learning Machine (ELM), Decision Tree (DT), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were examined and used in the application. Theory and Methods:The two basic units of the marble classification process, feature extraction and classification, are explained step by step. The effect of input parameters used in classification algorithms on classification success is examined and given on the GUI. GUI can be a basis for industrial applications by giving software design architecture. Feature extraction processes are Python based, GUI and classification algorithms are developed based on Java. In the feature extraction process, advanced Python mathematical functions and Swing, Java's advanced interface development library, were used. In addition, by using Weka's Java library for classification functions, classification functions can be integrated into GUI in an easy and modular way.Results: According to the proposed feature extraction and classification algorithms, by learning the images of marble species belonging to 4 different classes, 20 from each, 90-95% crossverified classification results were achieved. After these results are obtained, the GUI 'design architecture has been removed and developed for the end user. Conclusion:The developed GUI offers a solution to a big problem for industrial use. It is aimed to reach the most accurate result by determining the class according to the majority result by running 4 different classifiers simultaneously. If the features of the marble image whose class is determined do not meet the company's criteria, the training set development module developed on the GUI can be automatically added to the training set with its class and features. This enables training sets to be created that provide higher classification performance without any workload.

show abstract

Extended multi-structure local binary pattern for high-resolution image scene classification

Cited by 9 publications

References 15 publications

Review of local binary pattern operators in image feature extraction

Review of local binary pattern operators in image feature extraction

A survey on local binary pattern and gabor filter as texture descriptors of smart profiling systems

Grafiksel Arayüz Tabanlı Mermer Sınıflandırma Uygulaması Geliştirme

Contact Info

Product

Resources

About