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About 3,851 papers for “Local binary patterns”

Dominant Local Binary Patterns for Texture Classification

Shu Liao, Meng Law, Albert C. S. Chung · IEEE Transactions on Image Processing · 2009

This paper proposes a novel approach to extract image features for texture classification. The proposed features are robust to image rotation, less sensitive to histogram equalization and noise. It comprises of two sets of features: dominant local binary patterns (DLBP) in a texture image and the supplementary features extracted by using the circularly symmetric Gabor filter responses. The dominant local binary pattern method makes use of the most frequently occurred patterns to capture descriptive textural information, while the Gabor-based features aim at supplying additional global textural information to the DLBP features. Through experiments, the proposed approach has been intensively evaluated by applying a large number of classification tests to histogram-equalized, randomly rotated and noise corrupted images in Outex, Brodatz, Meastex, and CUReT texture image databases. Our method has also been compared with six published texture features in the experiments. It is experimentally demonstrated that the proposed method achieves the highest classification accuracy in various texture databases and image conditions.

Cited by 781

Invariant texture analysis through Local Binary Patterns

Rodrigo Nava, Gabriel Cristóbal, Boris Escalante‐Ramírez · arXiv (Cornell University) · 2011

In many image processing applications, such as segmentation and classification, the selection of robust features descriptors is crucial to improve the discrimination capabilities in real world scenarios. In particular, it is well known that image textures constitute power visual cues for feature extraction and classification. In the past few years the local binary pattern (LBP) approach, a texture descriptor method proposed by Ojala et al., has gained increased acceptance due to its computational simplicity and more importantly for encoding a powerful signature for describing textures. However, the original algorithm presents some limitations such as noise sensitivity and its lack of rotational invariance which have led to many proposals or extensions in order to overcome such limitations. In this paper we performed a quantitative study of the Ojala's original LBP proposal together with other recently proposed LBP extensions in the presence of rotational, illumination and noisy changes. In the experiments we have considered two different databases: Brodatz and CUReT for different sizes of LBP masks. Experimental results demonstrated the effectiveness and robustness of the described texture descriptors for images that are subjected to geometric or radiometric changes.

Cited by 7Open access

Local Binary Patterns for Document Forgery Detection

Francisco Cruz, Nicolas Sidère, Mickaël Coustaty et al. · 2017

Document forgery is an increasing problem for both the public administration and private companies. It represents substantial losses in time and economical resources. Classical solutions to this problem such as watermarks or other integrated security patterns can not be applied in general for any unknown incoming document due to the large variability on types of documents. In that scenario it is important to resort to forensic techniques to seek and analyze inconsistencies on the intrinsic features of the document image. In this paper we present a classification-based approach for forgery detection. We use uniform Local Binary Patterns (LBP) to capture discriminant texture features that are common on forged regions. Besides, we combine multiple descriptors from neighboring regions to model contextual information. Results using Support Vector Machines (SVM) for patch classification show that we are able to detect several types of forgeries in a wide range of types of documents.

Cited by 45

Plant Species Recognition using Local Binary and Local Directional Patterns

Divyan Hirasen, Serestina Viriri · 2020

Automatic plant species recognition is an intriguing and challenging problem which has vital applications in important real-world areas such as determining plant health, identifying new species and conserving biodiversity. Therefore, deriving an effective plant species representation from images is vital for successful species recognition. In this article the robustness of computer vision texture-based recognition techniques, such as Local Binary and Local Directional Pattern implementations, are evaluated for capturing discriminant textural features of leaf images. Furthermore, classification is done using both K-Nearest Neighbour and Support Vector Machine classifiers on the Swedish and Flavia leaf datasets. The results show that both these local patterns are successful at encoding dominant textural characteristics of leaf images to uniquely identify different plant species. The highest result obtained with the proposed methodology on the Flavia and Swedish leaf datasets are 96.94 % and 98.22 % identification accuracy respectively.

Cited by 4

Binary image classification using genetic programming based on local binary patterns

Harith Al-Sahaf, Mengjie Zhang, Mark Johnston · 2013

Image classification represents an important task in machine learning and computer vision. To capture features covering a diversity of different objects, it has been observed that a sufficient number of learning instances are required to efficiently estimate the models' parameter values. In this paper, we propose a genetic programming (GP) based method for the problem of binary image classification that uses a single instance per class to evolve a classifier. The method uses local binary patterns (LBP) as an image descriptor, support vector machine (SVM) as a classifier, and a one-way analysis of variance (ANOVA) as an analyser. Furthermore, a multi-objective fitness function is designed to detect distinct and informative regions of the images, and measure the goodness of the wrapped classifiers. The performance of the proposed method has been evaluated on six data sets and compared to the performances of both GP based (Two-tier GP and conventional GP) and non-GP (Naïve Bayes, Support Vector Machines and hybrid Naïve Bayes/Decision Trees) methods. The results show that a comparable or significantly better performance has been achieved by the proposed method over all methods on all of the data sets considered.

Cited by 14

Local Binary Patterns Calculated over Gaussian Derivative Images

Varun Jain, James L. Crowley, Augustin Lux · HAL (Le Centre pour la Communication Scientifique Directe) · 2014

In this paper we present a new static descriptor for facial image analysis. We combine Gaussian derivatives with Local Binary Patterns to provide a robust and powerful descriptor especially suited to extracting texture from facial images. Gaussian features in the form of image derivatives form the input to the Linear Binary Pattern(LBP) operator instead of the original image. The proposed descriptor is tested for face recognition and smile detection. For face recognition we use the CMU-PIE and the YaleB+extended YaleB database. Smile detection is performed on the benchmark GENKI 4k database. With minimal machine learning our descriptor outperforms the state of the art at smile detection and compares favourably with the state of the art at face recognition.

Cited by 7Open access

Enhancing Local Binary Patterns Distinctiveness for Face Representation

M Ghahramani, Wei‐Yun Yau, E.K. Teoh · DR-NTU (Nanyang Technological University) · 2011

The Local Binary pattern (LBP) is a well-known feature and has been widely used for human identification. However, the amount of information extracted is limited which reduces the LBP discriminative power. Recently, some enhancements have been proposed by adding preprocessing stages or considering more neighbor pixels to enrich the extracted feature. In this paper, we propose Uniformly-sampled Thresholds for LBP (UTLBP) operator that increases the richness of information derived from the LBP feature. It outperforms other features in various probe sets of the large CAS-PEAL database for face recognition. Moreover, we collected a database of 25 families to verify the superiority of the proposed feature in the family verification. Results show that using the UTLBP, the total error in face recognition and family verification is reduced up to 8% and 3% respectively comparing to the state of the art LBP. It improves the missing family member verification performance up to 3% where, contrary to expectation, increasing the LBP operator radius worsens the performance by 2%.

Cited by 5Open access

Improving Facial Recognition Accuracy Using Local Binary Patterns

Prof Neeraj Bhargava, Pramod Singh Rathore, Deepshikha Saxena et al. · 2023

The proposed method involves pre-processing of the dataset, which includes normalization and resizing of the images to a standard size. Local binary pattern is then used to extract features from the pre-processed images. These features are used to train a machine learning model, specifically a support vector machine, to recognize faces in the images. The effectiveness of the proposed approach was evaluated using a publicly available dataset, and the results indicate a high level of accuracy in face detection, with a recognition rate of over 90%. The approach was also tested on images with varying contrast and rotation angles, and the results demonstrate its ability to accurately identify faces under such conditions. The proposed method has practical applications in various areas such as surveillance, access control, and forensics. It has the potential to be integrated into security systems for real-time face recognition and identification, improving the accuracy and efficiency of such systems. Furthermore, the method can be extended to include more complex features for more accurate and robust face detection. Overall, this research contributes to the growing body of knowledge on face detection techniques and their practical applications.

Cited by 13

Facial expression recognition based on local binary patterns

Xiaoyi Feng, Matti Pietikäinen, Abdenour Hadid · Pattern Recognition and Image Analysis · 2007

In this paper, a novel approach to automatic facial expression recognition from static images is proposed. The face area is first divided automatically into small regions, from which the local binary pattern (LBP) histograms are extracted and concatenated into a single feature histogram, efficiently representing facial expressions—anger, disgust, fear, happiness, sadness, surprise, and neutral. Then, a linear programming (LP) technique is used to classify the seven facial expressions. Experimental results demonstrate an average expression recognition accuracy of 93.8% on the JAFFE database, which outperforms the rate of all other reported methods on the same database.

Cited by 102

Pengenalan Ekspresi Wajah Menggunakan Metode Local Binary Patterns

DonyTriKusuma Hadi · 2012

Pengenalan ekspresi wajah digunakan untuk melakukan pengenalan fitur-fitur ekspresi dari sebuah citra masukan yang berupa citra wajah, dimana fitur-fitur tersebut akan digunakan dalam pencocokan kategori ekspresi wajah. Proses pengenalan fitur dilakukan dengan menggunakan metode Local Binary Patterns dan proses pencocokan fitur dilakukan dengan menggunakan metode Template Mactching. Pada penelitian ini digunakan sebanyak 158 citra yang diambil dari database JAFFE, dimana citra tersebut telah dikelompokkan menjadi 7 ekspresi yaitu senang, sedih, marah, terkejut, takut, jijik, dan netral. Parameter yang diamati adalah pengaruh jumlah data latih, pemilihan histogram LBP, dan jumlah k hasil pencocokan terkecil terhadap jumlah pengenalan benar. Sistem dapat bekerja dengan baik, dengan tingkat akurasi tertinggi pengenalan ekspresi wajah sebanyak 59,09%, 61,36%, dan 60,61% yang merupakan hasil pegenalan ekspresi senang dengan menggunakan tipe histogram LBP 256bin. Pemilihan citra dengan yang digunakan sebagai data latih dan pemilihan tipe histogram LBP sangat mempengaruhi hasil pengenalan ekspresi wajah menggunakan metode Local Binary Patterns. Sedangkan jumlah data latih dan nilai k hanya sedikit berpengaruh pada hasil pengenalan.

Cited by 0

Extracting Local Binary Patterns with MIPA4k vision processor

Olli Lahdenoja, Jonne Poikonen, Mika Laiho · 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010) · 2010

In this paper we show how the MIPA4k vision processor can be used to extract and process local image features called Local Binary Patterns (LBPs). Using these low level image features more advanced higher level processing can be performed. LBP histograms can be extracted with wide field of potential applications. Also, directional thresholding results, which are part of LBP extraction process, can be used for higher level processing such as face localization. The MIPA4k vision processor consists of an array of 64 × 64 mixed-mode processing units with on-chip image capture sensors.

Cited by 4

Face Recognition with Local Binary Patterns Bachelor Assignment

B. K. Julsing · 2008

This report is about a bachelor assignment in which research has been done to face recognition with Local Binary Patterns (LBP). LBP is a very powerful method to describe the texture and shape of a digital image. Therefore it appeared to be suitable for feature extraction in face recognition systems. A face image is first divided into small regions from which LBP histograms are extracted and then concatenated into a single feature vector. This feature vector forms an efficient representation of the face and can be used to measure similarities between images. The report describes the principles of the method and how exactly it can be implemented to perform face recognition. Experiments have been carried out on the different sets of the FERET database. High recognition rates are obtained, especially compared to other face recognition methods. Also a few extensions are investigated to further improve the performance of the method. It appeared that LBP can easily be combined with Principal Component Analysis to reduce the length of the feature vector, while the recognition performance only slightly decreases. i

Cited by 0

Analysis of Document Images through Local Binary Patterns

N. Anusha, M. Siva Sindhuri · Indian Journal of Science and Technology · 2017

Objective: To develop a robust Local Binary Pattern (LBP) method with ENI (Edge pixels, Noisy pixels and Interior pixels) features for enhancement and binarization of ancient document images. Methods: The Local Binary Pattern method is used for improving the image quality by the extraction optimal image features. This method works efficiently for noisy and low contrasted ancient document image. The foreground and background of the low contrasted ancient document images are overlapped and in some documents back ground may be very complex. It is very difficult to separate background from foreground of the document Images. These flaws can be removed effectively by robust Local Binary Pattern method. This method can separate the foreground and background of the ancient document images by enhancing the quality. Findings: Thus the proposed LBP method achieves better results even when more level of noise is added to the image. It enhances and extracts the text in the document image by giving the robust results. The experimental results show that the proposed method produces better results than traditional methods. Applications: The proposed Local Binary Pattern method can be used to improve and extract the optimal image features from the ancient or low contrasted and noisy document images. Keywords: Ancient Documents, Binarization, Image Classification, Local Binary Pattern (LBP)

Cited by 2Open access

Inspection of metallic surfaces using Local Binary Patterns

Marcelo Mansano, Leopoldo Pavesi, Luiz S. Oliveira et al. · 2011

In this paper we propose the use of a texture feature called Linear Binary Patterns to inspect steal surfaces. To assess the proposed method, we have build two different databases. The first one contains 996 color images of steel bars illuminated with black light, where the defects were highlighted using penetrating liquid. The second dataset is composed of 1141 gray-scale images of steel bars without highlight. Comprehensive experiments using three different classifiers show that the proposed feature set is able to detect 91.5% and 95.6% of the defects on the first and second databases, respectively.

Cited by 10