Adaptive Local Binary Pattern Research Articles

Detection of neurodegenerative diseases using hybrid MODWT and adaptive local binary pattern

Detection of neurodegenerative diseases using hybrid MODWT and adaptive local binary pattern

Scale and pattern adaptive local binary pattern for texture classification

Local binary pattern (LBP) with a fixed sampling template is sensitive to scale changes. Furthermore, under rotation changes or noise corruptions, one uniform LBP pattern can be corrupted to fall into a non-uniform pattern which loses its discrimination power to describe the corresponding texture feature. To overcome these two main drawbacks, we propose a scale and pattern adaptive local binary pattern (SPALBP). Firstly, in the gradient-based sampling radius adaptive scheme, eight directional adaptive sampling radius of each center pixel can be obtained by using its eight Kirsch gradient values. Secondly, in the noise and rotation robust neighborhood sampling scheme, three neighborhood sampling templates are used to extract three kinds of averaging neighborhood pixels. Thirdly, for each center pixel, three kinds of LBPriu2 patterns can be extracted by sampling these three kinds of averaging neighborhood pixels along eight directional adaptive sampling radius. Finally, an optimal SPALBP uniform pattern can be adaptively selected from these three LBPriu2 patterns. Hence, all SPALBP patterns show more robustness against scale changes, rotation changes and noise corruptions. Extensive experiments are conducted on four standard texture databases: Outex, UIUC, CUReT and XU_HR. Comparing with state-of-the-art LBP-based variants, the proposed SPALBP method consistently shows superior performance both in dramatic environment changes and high-levels of noise conditions, meanwhile it maintains a lower texture feature dimension.

Multi‐scaled feature fusion enabled convolutional neural network for predicting fibrous dysplasia bone disorder

AbstractBackgroundConvolutional Neural Network (CNN) has exciting advantages in the processing of medical images. It produces the denoised and segmented results of images. When an image has orientation and scaling issues over the bundle that is not correctly recognized by the classifier.MethodsTo overcome these issues, we propose Extreme Deep Learning on Feature Fusion Convolutional Neural Network (EDL‐FFCNN) for classification. Feature fusion workflow enhances the ability to learn the feature from the inconsistent behaviour. And, to learn the texture, colour, and intensities of an image. The initial step of the FFCNN framework is to reconstruct the images using filtered back projection. It reduces the noise in edges and cross‐sectional areas. Contrast Limited Adaptive Histogram Equalization and Local Binary Pattern is applied to an image to adjust and extract the intensity and textural features of an image. The proposed FFCNN is a decision‐based approach that consists of two persistent classifiers. One, the convolutional features are combined with manually extracted multi‐scaled features. Subsequently, the normalization techniques are built‐in every layer to adjust the input dimensions. It also helps to reduce the training time. As an outcome of Image abnormalities is considered as knowledge transfer to the CNN.ResultsThe fused features on the CNN classifier produce the abnormality prediction results. Second, the Inception V2 CNN classifier framework classifies the bone images with extracted features. With these two comparative‐classification results, the major counting has been taken as the decision for final abnormality prediction. The proposed model gives improved accuracy of 95% for the elbow, 93% for the forearm, 92% for the shoulder, 92% for the wrist, 93% for the finger, 92% for humerus images.

Seizure Detection with Local Binary Pattern and CNN Classifier

The present work proposes two novel approaches namely One Dimensional adaptive average Local Binary Pattern (1-D AaLBP) and One-Dimensional adaptive difference Local Binary Pattern (1-D AdLBP) for feature extraction from EEG signals and Convolutional Neural Network (CNN) for classification of EEG signals. Both the proposed feature extraction methods are computationally easy to implement. In the first step the histograms are formed from the extracted patterns, after that feature vectors of the histogram are given as input to the classifier. Two benchmark EEG datasets such as Bonn and CHB-MIT are employed for conducting experiments for comparing the performances of the proposed method with other existing research works. The performance measures such as sensitivity, specificity, classification accuracy and execution time are used for evaluating the proposed methods. It is learned from the experiments conducted that among various methods the proposed method provides improved performance in terms of sensitivity, specificity, classification accuracy and execution time.

Face recognition based on full convolutional neural network based on transfer learning model

Deep learning has achieved a great success in face recognition (FR), however, little work has been done to apply deep learning for face photo-sketch recognition. This paper proposes an adaptive scale local binary pattern extraction method for optical face features. The extracted features are classified by Gaussian process. The most authoritative optical face test set LFW is used to train the trained model. Test, the test accuracy is 98.7%. Finally, the face features extracted by this method and the face features extracted from the convolutional neural network method are adapted to sketch faces through transfer learning, and the results of the adaptation are compared and analyzed. Finally, the paper tested the open-source sketch face data set CUHK Face Sketch database(CUFS) using the multimedia experiment of the Chinese University of Hong Kong. The test result was 97.4%. The result was compared with the test results of traditional sketch face recognition methods. It was found that the method recognized High efficiency, it is worth promoting.

Feature-Level Fusion of Finger Vein and Fingerprint Based on a Single Finger Image: The Use of Incompletely Closed Near-Infrared Equipment

Due to its portability, convenience, and low cost, incompletely closed near-infrared (ICNIR) imaging equipment (mixed light reflection imaging) is used for ultra thin sensor modules and have good application prospects. However, equipment with incompletely closed structure also brings some problems. Some finger vein images are not clear and there are sparse or even missing veins, which results in poor recognition performance. For these poor quality ICNIR images, however, there is additional fingerprint information in the image. The analysis of ICNIR images reveals that the fingerprint and finger vein in a single ICNIR image can be enhanced and separated. We propose a feature-level fusion recognition algorithm using a single ICNIR finger image. Firstly, we propose contrast limited adaptive histogram equalization (CLAHE) and grayscale normalization to enhance fingerprint and finger vein texture, respectively. Then we propose an adaptive radius local binary pattern (ADLBP) feature combined with uniform pattern to extract the features of fingerprint and finger vein. It solves the problem that traditional local binary pattern (LBP) is unable to describe the texture features of different sizes in ICNIR images. Finally, we fuse the feature vectors of ADLBP block histogram for a fingerprint and finger vein, and realize feature-layer fusion recognition by a threshold decision support vector machine (T-SVM). The experimentation results showed that the performance of the proposed algorithm was noticeably better than that of the single model recognition algorithm.

Smoke vehicle detection based on multi-feature fusion and hidden Markov model

Existing smoke vehicle detection methods and vision-based smoke detection methods are vulnerable to false alarms. This paper presents an automatic smoke vehicle detection method based on multi-feature fusion and hidden Markov model (HMM). In this method, we first detect moving objects using an improved visual background extractor (ViBe) algorithm and obtain smoke-colored blocks using color histogram features in the HSI (hue, saturation, and intensity) color space. The adaptive scale local binary pattern (AS-LBP) and the discriminative edge orientation histogram (disEOH) are proposed and combined to characterize the smoke-colored blocks. More specifically, the proposed AS-LBP, a texture feature descriptor, is based on the quadratic fitting of our labelled data to obtain the best scale. The proposed disEOH, a gradient-based feature descriptor, is robust to noise by extracting discriminative edge information using Gaussian filters and principal component analysis (PCA). The discrete cosine transform (DCT) is employed to extract frequency domain information from the region fused by smoke blocks. To utilize the dynamic features, the HMMs are employed to analyze and classify the smoke-colored block sequences and region sequences in continuous frames. The experimental results show that the proposed method achieves better performances than existing smoke detection methods, especially achieves lower false alarms.

Combining Modified LBP and Weighted SRC for Palmprint Recognition

In order to extract invariant features in the palmprint transformation of scale, rotation and affine distortion, a coarse-to-fine palmprint recognition method is proposed by combining the weighted adaptive center symmetric local binary pattern (WACS-LBP) and weighted sparse representation based classification (WSRC). The method consists of coarse and fine stages. In the coarse stage, using the similarity between the test sample and one sample of each training class, most of the training classes could be excluded and a small number of candidate classes of the test sample are reserved. Thus, the original classification problem becomes clear and simple. In the fine stage, the robust rotation invariant weighted histogram feature vector is extracted from each candidate sample and the test sample by WACS-LBP, and the weighted sparse representation optimal problem is constructed by the similarity between the test sample and each candidate training sample, and the test sample is recognized by the minimum residual. The proposed method is tested and compared with the existing algorithms on the PolyU and CASIA database. The experimental results illustrate better performance and rationale interpretation of the proposed method.

Combining weighted adaptive CS-LBP and local linear discriminant projection for gait recognition

With the increasing demands of the remote surveillance system, the gait based personal identification research has obtained more and more attention from biometric recognition researchers. The gait sequence is easier to be affected by factors than other biometric feathers. In order to achieve better performance of the gait based identification system, in the paper, a local discriminant gait recognition method is proposed by integrating weighted adaptive center symmetric local binary pattern (WACS-LBP) with local linear discriminate projection (LLDP). The proposed method consists of two stages. In the first stage, the robust local weighted histogram feature vector is extracted from each gait image by WACS-LBP. In the second stage, the dimensionality of the extracted feature vector is reduced by LLDP. The highlights of the proposed method are (1) the extracted feature is robust to rotation invariant, and is also tolerant to illumination and pose changes; (2) the low dimensional feature vector reduced by LLDP can preserve the discriminating ability; and (3) the small-sample-size (SSS) problem is avoided naturally. The proposed method is validated and compared with the existing algorithms on a public gait database. The experimental results show that the proposed method is not only effective, but also can be clearly interpreted.

A new copy-move forgery detection algorithm using image preprocessing procedure

One of the simplest ways of digital image forgery is a copy-move attack. The copy-move process consists of three stages: copy selected image fragment from one place, transform it using some image processing algorithm and paste it to another place of the same image. Two main approaches to copy-move detection exist nowadays: feature-based and hash-based. Most of the algorithms are developed according to the feature-based approach, whereas the hash-based approach is used only for plain copy-move detection (when the copied image fragment is not transformed). However, the main advantage of hash-based algorithms is low computational complexity. In this paper, we propose a new hash-based copy-move detection algorithm that can be applied to transformed duplicates detection due to a special preprocessing procedure. This procedure implements initial image transformation to incorporate the changes (affine transforms are not considered in this paper), produced by a transform algorithm on the second stage. Several preprocessing procedures are compared during experiments: image intensity range reduction, gradient calculation, expansion in orthonormal basis, adaptive linear contrast enhancement and local binary pattern.

An adaptive local binary pattern for 3D hand tracking

Ever since the availability of real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor, the performance of gesture recognition can be largely enhanced. However, since conventional two-dimensional (2D) image based feature extraction methods such as local binary pattern (LBP) generally use texture information, they cannot be applied to depth or range image which does not contain texture information. In this paper, we propose an adaptive local binary pattern (ALBP) for effective depth images based applications. Contrasting to the conventional LBP which is only rotation invariant, the proposed ALBP is invariant to both rotation and the depth distance in range images. Using ALBP, we can extract object features without using texture or color information. We further apply the proposed ALBP for hand tracking using depth images to show its effectiveness and its usefulness. Our experimental results validate the proposal.

Three-dimensional Head Tracking Using Adaptive Local Binary Pattern in Depth Images

Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.

Medical image classification using spatial adjacent histogram based on adaptive local binary patterns

Medical image recognition is an important task in both computer vision and computational biology. In the field of medical image classification, representing an image based on local binary patterns (LBP) descriptor has become popular. However, most existing LBP-based methods encode the binary patterns in a fixed neighborhood radius and ignore the spatial relationships among local patterns. The ignoring of the spatial relationships in the LBP will cause a poor performance in the process of capturing discriminative features for complex samples, such as medical images obtained by microscope. To address this problem, in this paper we propose a novel method to improve local binary patterns by assigning an adaptive neighborhood radius for each pixel. Based on these adaptive local binary patterns, we further propose a spatial adjacent histogram strategy to encode the micro-structures for image representation. An extensive set of evaluations are performed on four medical datasets which show that the proposed method significantly improves standard LBP and compares favorably with several other prevailing approaches.

A novel approach for SEMG signal classification with adaptive local binary patterns.

Feature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals.

Texture descriptors based on adaptive neighborhoods for classification of pigmented skin lesions

Different texture descriptors are proposed for the automatic classification of skin lesions from dermoscopic images. They are based on color texture analysis obtained from (1) color mathematical morphology (MM) and Kohonen self-organizing maps (SOMs) or (2) local binary patterns (LBPs), computed with the use of local adaptive neighborhoods of the image. Neither of these two approaches needs a previous segmentation process. In the first proposed descriptor, the adaptive neighborhoods are used as structuring elements to carry out adaptive MM operations which are further combined by using Kohonen SOM; this has been compared with a nonadaptive version. In the second one, the adaptive neighborhoods enable geometrical feature maps to be defined, from which LBP histograms are computed. This has also been compared with a classical LBP approach. A receiver operating characteristics analysis of the experimental results shows that the adaptive neighborhood-based LBP approach yields the best results. It outperforms the nonadaptive versions of the proposed descriptors and the dermatologists’ visual predictions.

Estimation of Crowd Density Based on Adaptive LBP

In recent years, mass incidents occurred frequently. In order to identify and warn the incidents proactively and timely. To this problem, we propose an algorithm based on adaptive LBP to estimate the crowd density. Firstly, use three-dimensional Hessian matrix to detect characteristic point. Secondly, use improved adaptive LBP to extract the dynamic texture and analyze it, then get the local feature. Thirdly, learn for global characteristic vectors, and then estimate the density level with support vector machine (SVM). Through simulation comparison, the density estimation method is more accurate and more real-time.

An efficient HOG–ALBP feature for pedestrian detection

Histograms of oriented gradients (HOG) is the most successful feature descriptor in pedestrian detection; however, it is limited because of only considering the gradient. It has a certain false-positive rate on some examples, which have a lot of parallel vertical components (looks like a leg or a body) due to lacking of texture feature. This paper proposes a method to combine a cell-structured HOG feature and adaptive local binary pattern feature to solve the problem that HOG is vulnerable to the interference of vertical background gradient information in pedestrian detection. In addition, we use a fast method to utilize sub-cell-based interpolation to efficiently compute HOG feature for each block. Training the combination feature to get a discriminative model by bootstrapped linear support vector machine. Experimental results on the INRIA dataset have demonstrated the effectiveness and efficiency of the proposed method.

Adaptive local binary pattern with oriented standard deviation (ALBPS) for texture classification

A new method to describe texture images using a hybrid combination of local and global texture descriptors is proposed in this paper. In this regard, a new adaptive local binary pattern (ALBP) descriptor is presented in order to carry out the local description. It is built by adding oriented standard deviation information to an ALBP descriptor in order to achieve a more complete representation of the images, and hence, it has been called adaptive local binary pattern with oriented standard deviation (ALBPS). Regarding semen vitality assessment, ALBPS outperformed previous literature works with an 81.88% accuracy and also yielded higher hit rates than the LBP and ALBP baseline methods. Concerning the global description of the images, several classical texture algorithms were tested and a descriptor based on wavelet transform and Haralick feature extraction (wavelet concurrent feature 13 (WCF13)) obtained the best results. Both local and global descriptors were combined, and the classification was carried out with a support vector machine. Two data sets have been evaluated: textures under varying illumination, pose and scale (KTH-TIPS) 2a data set and a second spermatozoa boar data set used to distinguish between dead or alive sperm heads. Therefore, our proposal is novel in three ways. First, a new local feature extraction method ALBPS is introduced. Second, a hybrid method combining the proposed local ALBPS and a global descriptor is presented, outperforming our first approach and all other methods evaluated for this problem. Third, texture classification accuracy is greatly improved with the two former texture descriptors presented. F score and accuracy values were computed in order to measure the performance. The best overall result was yielded by combining ALBPS with WCF13, reaching an F score = 0.886 and an accuracy of 85.63% in the spermatozoa data set and an 84.45% of hit rate in the KTH-TIPS 2a.

Wavelet-Based Multiscale Adaptive LBP with Directional Statistical Features for Recognizing Artificial Faces

Recognizing avatar faces is a very important issue for the security of virtual worlds. In this paper, a novel face recognition technique based on the wavelet transform and the multiscale representation of the adaptive local binary pattern (ALBP) with directional statistical features is proposed to increase the accuracy rate of recognizing avatars in different virtual worlds. The proposed technique consists of three stages: preprocessing, feature extraction, and recognition. In the preprocessing and feature extraction stages, wavelet decomposition is used to enhance the common features of the same subject of images and the multiscale ALBP (MALBP) is used to extract representative features from each facial image. Then, in the recognition stage the wavelet MALBP (WMALBP) histogram dissimilarity with statistical features of each test image and each class model is used within the nearest neighbor classifier to improve the classification accuracy of the WMALBP. Experiments conducted on two virtual world avatar face image datasets show that our technique performs better than LBP, PCA, multiscale local binary pattern, ALBP, and ALBP with directional statistical features (ALBPF) in terms of the accuracy and the time required to classify each facial image to its subject.

Image retrieval and classification using adaptive local binary patterns based on texture features

In this study, adaptive local binary patterns (ALBP) are proposed for image retrieval and classification. ALBP are based on texture features for local binary patterns. The texture features were used to propose an adaptive local binary patterns histogram (ALBPH) and gradient for adaptive local binary patterns (GALBP) in this study. Two texture features are most useful for describing the relationship in a local neighbourhood. ALBPH shows the texture distribution of an image by identifying and employing the difference between the centre pixel and the neighbourhood pixel values. In the GALBP, the gradient for each pixel is computed and the sum of the gradient of the ALBP number is adopted as an image feature. In this study, a set of colour and greyscale images were used to generate a variety of image subsets. Then, image retrieval and classification experiments were carried out for analysis and comparison with other methods. From the experimental results, the authors discovered that the proposed feature extraction method can effectively describe the characteristics of images in regard to texture image and image type. The image retrieval and classification experiments also produced better results than other methods.