LBP-based Features Research Articles

Swin-LBP: a competitive feature engineering model for urine sediment classification

Automated urine sediment analysis has become an essential part of diagnosing, monitoring, and treating various diseases that affect the urinary tract and kidneys. However, manual analysis of urine sediment is time-consuming and prone to human bias, and hence there is a need for an automated urine sediment analysis systems using machine learning algorithms. In this work, we propose Swin-LBP, a handcrafted urine sediment classification model using the Swin transformer architecture and local binary pattern (LBP) technique to achieve high classification performance. The Swin-LBP model comprises five phases: preprocessing of input images using shifted windows-based patch division, six-layered LBP-based feature extraction, neighborhood component analysis-based feature selection, support vector machine-based calculation of six predicted vectors, and mode function-based majority voting of the six predicted vectors to generate four additional voted vectors. Our newly reconstructed urine sediment image dataset, consisting of 7 distinct classes, was utilized for training and testing our model. Our proposed model has several advantages over existing automated urinalysis systems. Firstly, we used a feature engineering model that enables high classification performance with linear complexity. This means that it can provide accurate results quickly and efficiently, making it an attractive alternative to time-consuming and biased manual urine sediment analysis. Additionally, our model outperformed existing deep learning models developed on the same source urine sediment image dataset, indicating its superiority in urine sediment classification. Our model achieved 92.60% accuracy for 7-class urine sediment classification, with an average precision of 92.05%. These results demonstrate that the proposed Swin-LBP model can provide a reliable and efficient solution for the diagnosis, surveillance, and therapeutic monitoring of various diseases affecting the kidneys and urinary tract. The proposed model's accuracy, speed, and efficiency make it an attractive option for clinical laboratories and healthcare facilities. In conclusion, the Swin-LBP model has the potential to revolutionize urine sediment analysis and improve patient outcomes in the diagnosis and treatment of urinary tract and kidney diseases.

Environmental Sound Classification Using Local Binary Pattern and Audio Features Collaboration

This paper presents a new approach to classify environmental sounds using a texture feature local binary pattern (LBP) and audio features collaboration. To our knowledge, this is the first time that the LBP (or its variants), which has a proven track record in the field of image recognition and classification, has been generalized for 1D and combined with audio features for an environmental sound classification task. To this end, we have generalized and defined LBP-1D and local phase quantization (LPQ)-1D on the 1-dimensional (1D) audio signal and have applied the original LBP, the variance LBP (VARLBP) and the extended LBP (ELBP) thus generated to the spectrogram of the audio signal in order to model the sound texture. We have also extensively compared these new LBP-based features to the classical audio descriptors commonly used in environmental sound classification, such as MFCC, GFCC, CQT, chromagram, STE and ZCR. We have evaluated our algorithm on ESC-10 and ESC-50 datasets using classical machine learning algorithms, such as support vector machines (SVM), random forest and k-nearest neighbor (kNN). The results showed that the LBP features outperform the classical audio features. We mix the LBP features with the audio descriptors, and our best mixed model achieves state-of-the-art results for environmental sound classification: 88.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> on ESC-10 and 64.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> on ESC-50. Those results outperform the results of methods that used handcrafted features with classical machine learning algorithms and are similar to some convolutional neural network-based methods. Although our method is not the cutting edge of the state-of-the-art methods, it is faster than any convolutional neural network methods and represents a better choice when there is data scarcity or minimal computing power.

A Universal Image Forensics of Smoothing Filtering

Digital image smoothing filtering operations, including the average filtering, Gaussian filtering and median filtering are always used to beautify the forged images. The detection of these smoothing operations is important in the image forensics field. In this article, the authors propose a universal detection algorithm which can simultaneously detect the average filtering, Gaussian low-pass filtering and median filtering. Firstly, the high-frequency residuals are used as being the feature extraction domain, and then the feature extraction is established on the local binary pattern (LBP) and the autoregressive model (AR). For the LBP model, the authors exploit that both of the relationships between the central pixel and its neighboring pixels and the relationships among the neighboring pixels are differentiated for the original images and smoothing filtered images. A method is further developed to reduce the high dimensionality of LBP-based features. Experimental results show that the proposed detector is effective in the smoothing forensics, and achieves better performance than the previous works, especially on the JPEG images.

Central pixel selection strategy based on local gray-value distribution by using gradient information to enhance LBP for texture classification

Local binary pattern (LBP) has been successfully used in computer vision and pattern recognition applications, such as biomedical image analysis, remote sensing and image retrieval. However, the current LBP-based features, which assign a fixed sampling radius for all pixels in a single scale, completely ignore the fact that different central pixels actually have different local gray-value distributions and the proper sampling radius should be different for pixels. In this paper, we propose a novel and effective central pixel selection (CPS) strategy by using gradient information to classify central pixels of a texture image into different classes based on their local gray-value distributions. Then, we introduce this CPS strategy into the LBP framework and assign an adaptive sampling radius for each central pixel according to the class it belongs to. As a preprocessing step of LBP framework, this CPS strategy can also be integrated into any other LBP variants so as to extract more effective local texture features. Extensive experiments on five representative texture databases of Outex, UIUC, CUReT, UMD and ALOT validate the efficiency of the proposed central pixel selection (CPS) strategy, which can achieve almost 16% improvement over the original LBP and 1%–10% improvement compared with the best classification accuracy among other benchmarked state-of-the-art LBP variants.

Text/Non-Text Separation from Handwritten Document Images Using LBP Based Features: An Empirical Study

Isolating non-text components from the text components present in handwritten document images is an important but less explored research area. Addressing this issue, in this paper, we have presented an empirical study on the applicability of various Local Binary Pattern (LBP) based texture features for this problem. This paper also proposes a minor modification in one of the variants of the LBP operator to achieve better performance in the text/non-text classification problem. The feature descriptors are then evaluated on a database, made up of images from 104 handwritten laboratory copies and class notes of various engineering and science branches, using five well-known classifiers. Classification results reflect the effectiveness of LBP-based feature descriptors in text/non-text separation.

Multi-color space local binary pattern-based feature selection for texture classification

This paper deals with multi-color space texture classification. Two approaches are proposed and compared: a multi-color space histogram selection (MCSHS) and a multi-color space bin selection. These approaches select local binary pattern (LBP) histograms or LBP bins that have been processed from images coded in multiple color spaces. On the one hand, the proposed LBP-based feature selection scheme overcomes the difficulty of choosing a relevant color space, and on the other hand, it takes advantage of the specific properties of several color spaces by combining them. Experiments show that the MCSHS approach is relevant for color texture classification issues that require good performances whether in accuracy or classification computation time.

Non-deterministic approach to allay replay attack on iris biometric

Biometric-based verification system has emerged as a powerful authentication tool. Despite its advantages over traditional systems, it is prone to several attacks. These attacks may creep through the biometric system and may prove fatal if it is not robust enough. One such attack, known as replay attack, relates to replaying of illegally intercepted data has been least explored with respect to biometrics. The paper proposes a non-deterministic approach to iris recognition and attempts to show its utility in allaying replay attack over iris recognition system. The system determines robust iris regions for each eye using LBP-based feature extraction and involves the use of randomly selected subsets of these regions for authentication. These data, even if intercepted, are useless as the non-deterministic nature of technique will require a differently ordered subset of regions for each authentication. The performance of this system and its effectiveness in allaying replay attack has been shown experimentally. The results have been compared with existing state-of-art techniques with respect to iris recognition and replay attack. The impact of hill climbing attack on the proposed approach has also been discussed as it has been proved, by various researchers, to be critical to the performance of a biometric system.

Local binary circumferential and radial derivative pattern for texture classification

Building discriminative and robust texture representation to deal with the changes of texture appearance is a fundamental issue in texture classification. The Local Binary Pattern (LBP) and its variants gain a lot of attention during the past decade and achieve great success in texture description. However, the current existing LBP-based features which treat LBP as local differential or orientation gradient operator, exploited local orientation pattern or anisotropic structure information separately. In this paper, we investigate the theoretical scheme of local differential approximation on the polar coordinate system in order to build a new LBP-based descriptor which better takes into account both radial plus tangential components and derivative information. First, we present an operator called circumferential derivative (CD) based on the tangential information with different order of derivatives. Then, we present an operator called radial derivative (RD) based on the radial information with different order of derivatives. Both extract complementary information locally around a central pixel. A new descriptor, the local binary circumferential and radial derivative pattern (CRDP) is constructed to fuse both local circumferential and radial derivative features based on different orders as well as a global feature based on global difference (GD) of central pixel’s intensity. Extensive experiments on Outex, CUReT, KTH-TIPS and KTH-TIPS2-a texture datasets indicate that the proposed CRDP descriptor is discriminative and robust. The results obtained by the proposed CRDP descriptor outperforms more than twenty recent LBP-based state-of-the-art methods, including the best reported results in the literature for aforementioned texture datasets to the best of our knowledge.

Fusion of color histogram and LBP-based features for texture image retrieval and classification

The Local Binary Pattern (LBP) operator and its variants play an important role as the image feature extractor in the textural image retrieval and classification. The LBP-based operator extracts the textural information of an image by considering the neighboring pixel values. A single or join histogram can be derived from the LBP code which can be used as an image feature descriptor in some applications. However, the LBP-based feature is not a good candidate in capturing the color information of an image, making it is less suitable for measuring the similarity of color images with rich color information. This work overcomes this problem by adding an additional color feature, namely Color Information Feature (CIF), along with the LBP-based feature in the image retrieval and classification systems. The CIF and LBP-based feature adequately represent the color and texture features. As documented in the experimental result, the hybrid CIF and LBP-based feature presents a promising result and outperforms the existing methods over several image databases. Thus, it can be a very competitive candidate in retrieval and classification application.

Facial Expression Recognition From Image Sequence Based on LBP and Taylor Expansion

The aim of an automatic video-based facial expression recognition system is to detect and classify human facial expressions from image sequence. An integrated automatic system often involves two components: 1) peak expression frame detection and 2) expression feature extraction. In comparison with the image-based expression recognition system, the video-based recognition system often performs online detection, which prefers low-dimensional feature representation for cost-effectiveness. Moreover, effective feature extraction is needed for classification. Many recent recognition systems often incorporate rich additional subjective information and thus become less efficient for real-time application. In our facial expression recognition system, first, we propose the double local binary pattern (DLBP) to detect the peak expression frame from the video. The proposed DLBP method has a much lower-dimensional size and can successfully reduce detection time. Besides, to handle the illumination variations in LBP, logarithm-laplace (LL) domain is further proposed to get a more robust facial feature for detection. Finally, the Taylor expansion theorem is employed in our system for the first time to extract facial expression feature. We propose the Taylor feature pattern (TFP) based on the LBP and Taylor expansion to obtain an effective facial feature from the Taylor feature map. Experimental results on the JAFFE and Cohn-Kanade data sets show that the proposed TFP method outperforms some state-of-the-art LBP-based feature extraction methods for facial expression feature extraction and can be suited for real-time applications.

Discriminative local binary pattern

Local binary pattern (LBP) is widely used to extract image features as well as motion features in various visual recognition tasks. LBP is formulated in quite a simple form and thus enables us to extract effective features with a low computational cost. There, however, are some limitations mainly regarding sensitivity to noise and loss of image contrast information. In this paper, we propose a novel LBP-based feature extraction method to remedy those drawbacks without degrading the simplicity of the original LBP formulation. LBP is built upon encoding local pixel intensities into binary patterns which can be regarded as separating them into two modes (clusters). We introduce Fisher discriminant criterion to optimize the LBP coding for exploiting binary patterns more stably and discriminatively with robustness to noise. Besides, image contrast information is incorporated in a unified way by leveraging the discriminant score as a weight on the binary pattern; therefore, the prominent patterns, such as around edges, are emphasized. The proposed method is applicable to extract not only image features but also motion features by both efficiently decomposing a XYT volume patch into 2-D patches and employing the effective thresholding strategy based on the volume patch. In the experiments on various visual recognition tasks, the proposed method exhibits superior performance compared to the ordinary LBP and the other methods.

Color local binary patterns: compact descriptors for texture classification

Texture description is a challenging problem with color images. Despite some attempts to include colors in local binary patterns (LBPs), no proposal has emerged as a color counterpart of grayscale LBPs. This is because colors are defined by vectors that are not naturally ordered and several ways exist to compare them. We propose an LBP extension that takes the vector information of color into account due to a color order. As several color orders are available and the selection of the most suitable one is difficult, we combine two of them in a texture descriptor called “mixed color order LBPs.” This small-size feature provides good performance on several benchmark databases for two classification problems with regard to larger-size LBP-based features of color textures.

Infrared face recognition based on LBP co-occurrence matrix and partial least squares

Infrared face recognition, being light-independent, and not vulnerable to facial skin, expressions and posture, can avoid or limit the drawbacks of face recognition in visible light. This paper proposes an infrared face recognition method based on Local Binary Pattern (LBP) co-occurrence matrix. In traditional LBP-based features such as LBP histogram, space locations information, which is an important feature for recognition, is discarded. Considering such spatial relations in infrared faces, we introduce co-occurrence matrix of LBP codes, instead of histogram, to obtain more discriminative representation for location features. To cope with the high dimensions of LBP co-occurrence matrix, the final classifier formulates Partial Least Squares (PLS) regression for accurate classification. The experimental results show combination of LBP co-occurrence matrix and PLS achieve better infrared face recognition performance compared to state-of-the-art approaches.

Image orientation detection using LBP-based features and logistic regression

Many imaging applications require that images are correctly orientated with respect to their content. In this work we present an algorithm for the automatic detection of the image orientation that ...

A novel distribution-based feature for rapid object detection

The discriminative power of a feature has an impact on the convergence rate in training and running speed in evaluating an object detector. In this paper, a novel distribution-based discriminative feature is proposed to distinguish objects of rigid object categories from background. It fully makes use of the advantage of local binary pattern (LBP) that specializes in encoding local structures and statistic information of distribution from training data, which is utilized in getting optimal separating hyperplane. The proposed feature maintains the merit of simplicity in calculation and powerful discriminative ability to distinguish objects from background patches. Three LBP-based features are derived to adaptive projection ones, which are more discriminative than original versions. The asymmetric Gentle Adaboost organized in nested cascade structure constructs the final detector. The proposed features are evaluated on two different object categories: frontal human faces and side-view cars. Experimental results demonstrate that the proposed features are more discriminative than traditional Haarlike features and multi-block LBP (MBLBP) features. Furthermore they are also robust in monotonous variations of illumination.