Semi-supervised Deep Learning Model Research Articles

EEG power spectra parameterization and adaptive channel selection towards semi-supervised seizure prediction

Background:The seizure prediction algorithms have demonstrated their potential in mitigating epilepsy risks by detecting the pre-ictal state using ongoing electroencephalogram (EEG) signals. However, most of them require high-density EEG, which is burdensome to the patients for daily monitoring. Moreover, prevailing seizure models require extensive training with significant labeled data which is very time-consuming and demanding for the epileptologists. Method:To address these challenges, here we propose an adaptive channel selection strategy and a semi-supervised deep learning model respectively to reduce the number of EEG channels and to limit the amount of labeled data required for accurate seizure prediction. Our channel selection module is centered on features from EEG power spectra parameterization that precisely characterize the epileptic activities to identify the seizure-associated channels for each patient. The semi-supervised model integrates generative adversarial networks and bidirectional long short-term memory networks to enhance seizure prediction. Results:Our approach is evaluated on the CHB-MIT and Siena epilepsy datasets. With utilizing only 4 channels, the method demonstrates outstanding performance with an AUC of 93.15% on the CHB-MIT dataset and an AUC of 88.98% on the Siena dataset. Experimental results also demonstrate that our selection approach reduces the model parameters and training time. Conclusions:Adaptive channel selection coupled with semi-supervised learning can offer the possible bases for a light weight and computationally efficient seizure prediction system, making the daily monitoring practical to improve patients’ quality of life.

Gearbox fault diagnosis method based on improved semi-supervised MTDL and GAF

Aiming at the problem that it takes a long time and high cost to obtain complete labeled data under intelligent fault diagnosis and unlabeled data is not used. This paper proposes an improved semi-supervised mean teacher deep learning (MTDL) and Gramian angle field (GAF) fusion diagnostic method. This method fully utilizes a small number of labeled samples and a large number of unlabeled samples to deeply mine invisible fault features and potential physical correlations. At the same time, it solves the problem of losing the inter-data correlation structure when one-dimensional time series signals are used as inputs for neural networks. The GAF-MTDL method uses consistency regularization and modifies the network structure in the mean teacher algorithm into a semi-supervised deep learning model enhanced by WideResNet. The experimental results show that the proposed GAF-MTDL method saves a lot of manual labeling costs, improves the recognition accuracy and generalization ability, and can achieve excellent prediction accuracy with very little labeled data. In the end, the accuracy of planetary gear fault identification reached 98.22% under the labeling rate of 20%, and the accuracy of fault identification reached 99.98% through the verification of the bearing data set of Case Western Reserve University. The value of this research is to bring an efficient and low-cost technology to the field of industrial intelligent fault diagnosis, which can significantly improve the accuracy of fault identification.

Development and validation of a semi-supervised deep learning model for automatic retinopathy of prematurity staging

Retinopathy of prematurity (ROP) is currently one of the leading causes of infant blindness worldwide. Recently significant progress has been made in deep learning-based computer-aided diagnostic methods. However, deep learning often requires a large amount of annotated data for model optimization, but this requires long hours of effort by experienced doctors in clinical scenarios. In contrast, a large number of unlabeled images are relatively easy to obtain. In this paper, we propose a new semi-supervised learning framework to reduce annotation costs for automatic ROP staging. We design two consistency regularization strategies, prediction consistency loss and semantic structure consistency loss, which can help the model mine useful discriminative information from unlabeled data, thus improving the generalization performance of the classification model. Extensive experiments on a real clinical dataset show that the proposed method promises to greatly reduce the labeling requirements in clinical scenarios while achieving good classification performance.

Broiler Mobility Assessment via a Semi-Supervised Deep Learning Model and Neo-Deep Sort Algorithm.

Mobility is a vital welfare indicator that may influence broilers' daily activities. Classical broiler mobility assessment methods are laborious and cannot provide timely insights into their conditions. Here, we proposed a semi-supervised Deep Learning (DL) model, YOLOv5 (You Only Look Once version 5), combined with a deep sort algorithm conjoined with our newly proposed algorithm, neo-deep sort, for individual broiler mobility tracking. Initially, 1650 labeled images from five days were employed to train the YOLOv5 model. Through semi-supervised learning (SSL), this narrowly trained model was then used for pseudo-labeling 2160 images, of which 2153 were successfully labeled. Thereafter, the YOLOv5 model was fine-tuned on the newly labeled images. Lastly, the trained YOLOv5 and the neo-deep sort algorithm were applied to detect and track 28 broilers in two pens and categorize them in terms of hourly and daily travel distances and speeds. SSL helped in increasing the YOLOv5 model's mean average precision (mAP) in detecting birds from 81% to 98%. Compared with the manually measured covered distances of broilers, the combined model provided individual broilers' hourly moved distances with a validation accuracy of about 80%. Eventually, individual and flock-level mobilities were quantified while overcoming the occlusion, false, and miss-detection issues.

Rank Based Two Stage Semi-Supervised Deep Learning Model for X-Ray Images Classification

Rank Based Two Stage Semi-Supervised Deep Learning Model for X-Ray Images Classification

A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation

A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation

MTR-SDL: a soft computing based multi-tier rank model for shoulder X-ray classification.

Deep neural networks (DNN) effectiveness are contingent upon access to quality-labelled training datasets since label mistakes (label noise) in training datasets may significantly impair the accuracy of models trained on clean test data. The primary impediments to developing and using DNN models in the healthcare sector include the lack of sufficient label data. Labeling data by a domain expert are a costly and time-consuming task. To overcome this limitation, the proposed Multi-Tier Rank-based Semi-supervised deep learning (MTR-SDL) for Shoulder X-Ray Classification uses the small labelled dataset to generate a labelled dataset from unable dataset to obtain performance equivalent to approaches trained on the enormous dataset. The motivation behind the suggested model MTR-SDL approach is analogous to how physicians deal with unknown or suspicious patients in everyday life. Practitioners handle these questionable circumstances with the support of professional colleagues. Before initiating treatment, some patients consult with a range of skilled doctors. Patients are treated according to the most suitable professional diagnosis (vote count). In this article, we have proposed a new ensemble learning technique called "Rank based Ensemble Selection with machine learning models" (MTR-SDL) approach. In this technique, multiple machine learning models are trained on a labeled dataset, and their accuracy is ranked. A dynamic ensemble voting approach is then used to tag samples for each base model in the ensemble. The combination of these tags is used to generate a final tag for an unlabeled dataset. Our suggested MTR-SDL model has attained the best accuracy and specificity, sensitivity, precision, Matthew's correlation coefficient, false discovery rate, false positive rate, f1 score, negative predictive value, and false negative rate negative 92.776%, 97.376%, 86.932%, 96.192%, 85.644%, 3.808%, 2.624%, 91.072%, 90.85%, and 13.068% for unseen dataset, respectively. This approach has the potential to improve the performance of ensemble models by leveraging the strengths of multiple base models and selecting the most informative samples for each model. This study results in an improved Semi-supervised deep learning model that is more effective and precise.

Semi-Supervised Deep Kernel Active Learning for Material Removal Rate Prediction in Chemical Mechanical Planarization.

The material removal rate (MRR) is an important variable but difficult to measure in the chemical-mechanical planarization (CMP) process. Most data-based virtual metrology (VM) methods ignore the large number of unlabeled samples, resulting in a waste of information. In this paper, the semi-supervised deep kernel active learning (SSDKAL) model is proposed. Clustering-based phase partition and phase-matching algorithms are used for the initial feature extraction, and a deep network is used to replace the kernel of Gaussian process regression so as to extract hidden deep features. Semi-supervised regression and active learning sample selection strategies are applied to make full use of information on the unlabeled samples. The experimental results of the CMP process dataset validate the effectiveness of the proposed method. Compared with supervised regression and co-training-based semi-supervised regression algorithms, the proposed model has a lower mean square error with different labeled sample proportions. Compared with other frameworks proposed in the literature, such as physics-based VM models, Gaussian-process-based regression models, and stacking models, the proposed method achieves better prediction results without using all the labeled samples.

A fully-automatic semi-supervised deep learning model for difficult airway assessment

BackgroundDifficult airway conditions represent a substantial challenge for clinicians. Predicting such conditions is essential for subsequent treatment planning, but the reported diagnostic accuracies are still quite low. To overcome these challenges, we developed a rapid, non-invasive, cost-effective, and highly-accurate deep-learning approach to identify difficult airway conditions through photographic image analysis. MethodsFor each of 1000 patients scheduled for elective surgery under general anesthesia, images were captured from 9 specific and different viewpoints. The collected image set was divided into training and testing subsets in the ratio of 8:2. We used a semi-supervised deep-learning method to train and test an AI model for difficult airway prediction. ResultsWe trained our semi-supervised deep-learning model using only 30% of the labeled training samples (with the remaining 70% used without labels). We evaluated the model performance using metrics of accuracy, sensitivity, specificity, F1-score, and the area under the ROC curve (AUC). The numerical values of these four metrics were found to be 90.00%, 89.58%, 90.13%, 81.13%, and 0.9435, respectively. For a fully-supervised learning scheme (with 100% of the labeled training samples used for model training), the corresponding values were 90.50%, 91.67%, 90.13%, 82.25%, and 0.9457, respectively. When three professional anesthesiologists conducted comprehensive evaluation, the corresponding results were 91.00%, 91.67%, 90.79%, 83.26%, and 0.9497, respectively. It can be seen that the semi-supervised deep learning model trained by us with only 30% labeled samples can achieve a comparable effect with the fully supervised learning model, but the sample labeling cost is smaller. Our method can achieve a good balance between performance and cost. At the same time, the results of the semi-supervised model trained with only 30% labeled samples were very close to the performance of human experts. ConclusionsTo the best of our knowledge, our study is the first one to apply a semi-supervised deep-learning method in order to identify the difficulties of both mask ventilation and intubation. Our AI-based image analysis system can be used as an effective tool to identify patients with difficult airway conditions. Clinical trial registrationChiCTR2100049879 (URL: http://www.chictr.org.cn).

Construction of an epileptic seizure prediction model using a semi-supervised method of generative adversarial and long short term memory network combined with Stockwell transform

To propose a semi-supervised epileptic seizure prediction model (ST-WGAN-GP-Bi-LSTM) to enhance the prediction performance by improving time-frequency analysis of electroencephalogram (EEG) signals, enhancing the stability of the unsupervised feature learning model and improving the design of back-end classifier. Stockwell transform (ST) of the epileptic EEG signals was performed to locate the time-frequency information by adaptive adjustment of the resolution and retaining the absolute phase to obtain the time-frequency inputs. When there was no overlap between the generated data distribution and the real EEG data distribution, to avoid failure of feature learning due to a constant JS divergence, Wasserstein GAN was used as a feature learning model, and the cost function based on EM distance and gradient penalty strategy was adopted to constrain the unsupervised training process to allow the generation of a high-order feature extractor. A temporal prediction model was finally constructed based on a bi-directional long short term memory network (Bi-LSTM), and the classification performance was improved by obtaining the temporal correlation between high-order time-frequency features. The CHB-MIT scalp EEG dataset was used to validate the proposed patient-specific seizure prediction method. The AUC, sensitivity, and specificity of the proposed method reached 90.40%, 83.62%, and 86.69%, respectively. Compared with the existing semi-supervised methods, the propose method improved the original performance by 17.77%, 15.41%, and 53.66%. The performance of this method was comparable to that of a supervised prediction model based on CNN. The utilization of ST, WGAN-GP, and Bi-LSTM effectively improves the prediction performance of the semi-supervised deep learning model, which can be used for optimization of unsupervised feature extraction in epileptic seizure prediction.

End-to-end semi-supervised deep learning model for surface crack detection of infrastructures

Surface crack detection is essential for evaluating the safety and performance of civil infrastructures, and automated inspections are beneficial in providing objective results. Deep neural network-based segmentation methods have demonstrated promising potential in this purpose. However, the majority of these methods are fully supervised, requiring extensive manual labeling at pixel level, which is a vital but time-consuming and expensive task. In this paper, we propose a novel semi-supervised learning model for crack detection. The proposed model employs a modified U-Net, which has half the parameters of the original U-Net network to detect surface cracks. Comparison using 20 epochs shows that the modified U-Net network requires only 15% training time of the traditional U-net, but improves the accuracy by 20% upwards. On this basis, the proposed model (modified U-Net) is trained based on an updated strategy. At each stage, the trained model predicts and segments the unlabeled data images. The new strategy for updating the training datasets allows the model to be trained with limited labeled image data. To evaluate the performance of the proposed method, comprehensive image datasets consisting of the DeepCrack, Crack500 datasets those open to public, and an expanded dataset containing 2068 images of concrete bridge surface crack with our independent manual labels, are used to train and test the proposed method. Results show that the proposed semi-supervised learning method achieved quite approaching accuracies to the established fully supervised models using multiple accuracy indexes, however, the requirement for the labeled data reduces to 40%.

Research on the Guidance of Youth Labor Education Based on the "Combination of Education and Production Labor" Program Based on the Deep Learning Model.

At present, there is a lack of research on Marx's idea of “combining education and productive labor” and its guiding significance for youth labor education, and no effective teaching model has been formed. In response to this problem, this study proposes a semi-supervised deep learning model based on u-wordMixup (SD-uwM). When there is a shortage of labeled samples, semi-supervised learning uses a large number of unlabeled samples to solve the problem of labeling bottlenecks. However, since the unlabeled samples and labeled samples come from different fields, there may be quality problems in the unlabeled samples, which makes the generalization ability of the model worse., resulting in a decrease in classification accuracy. The model uses the u-wordMixup method to perform data augmentation on unlabeled samples. Under the constraints of supervised cross-entropy and unsupervised consistency loss, it can improve the quality of unlabeled samples and reduce overfitting. The comparative experimental results on the AGNews, THUCNews, and 20Newsgroups data sets show that the proposed method can improve the generalization ability of the model and also effectively improve the time performance. The study found that the SD-uwM model uses the u-wordMixup method to enhance the unlabeled samples and combines the idea of the Mean Teacher model, which can significantly improve the text classification performance. The SD-uwM model can improve the generalization ability and time performance of the model, respectively, 86.4 ± 1.3 and 90.5 ± 1.3. Therefore, the use of SD-uwM in Marx's program is of great practical significance for the guidance process of youth labor education.

Semi-Supervised Deep Learning Model for Efficient Computation of Optical Properties of Suspended-Core Fibers.

Suspended-core fibers (SCFs) are considered the best candidates for enhancing fiber nonlinearity in mid-infrared applications. Accurate modeling and optimization of its structure is a key part of the SCF structure design process. Due to the drawbacks of traditional numerical simulation methods, such as low speed and large errors, the deep learning-based inverse design of SCFs has become mainstream. However, the advantage of deep learning models over traditional optimization methods relies heavily on large-scale a priori datasets to train the models, a common bottleneck of data-driven methods. This paper presents a comprehensive deep learning model for the efficient inverse design of SCFs. A semi-supervised learning strategy is introduced to alleviate the burden of data acquisition. Taking SCF’s three key optical properties (effective mode area, nonlinear coefficient, and dispersion) as examples, we demonstrate that satisfactory computational results can be obtained based on small-scale training data. The proposed scheme can provide a new and effective platform for data-limited physical computing tasks.

Robust and Explainable Semi-Supervised Deep Learning Model for Anomaly Detection in Aviation

Identifying safety anomalies and vulnerabilities in the aviation domain is a very expensive and time-consuming task. Currently, it is accomplished via manual forensic reviews by subject matter experts (SMEs). However, with the increase in the amount of data produced in airspace operations, relying on such manual reviews is impractical. Automated approaches, such as exceedance detection, have been deployed to flag safety events which surpass a pre-defined safety threshold. These approaches, however, completely rely on domain knowledge and outcome of the SMEs’ reviews and can only identify purely threshold crossings safety vulnerabilities. Unsupervised and supervised machine learning approaches have been developed in the past to automate the process of anomaly detection and vulnerability discovery in the aviation data, with availability of the labeled data being their differentiator. Purely unsupervised approaches can be prone to high false alarm rates, while a completely supervised approach might not reach optimal performance and generalize well when the size of labeled data is small. This is one of the fundamental challenges in the aviation domain, where the process of obtaining safety labels for the data requires significant time and effort from SMEs and cannot be crowd-sourced to citizen scientists. As a result, the size of properly labeled and reviewed data is often very small in aviation safety and supervised approaches fall short of the optimum performance with such data. In this paper, we develop a Robust and Explainable Semi-supervised deep learning model for Anomaly Detection (RESAD) in aviation data. This approach takes advantage of both majority unlabeled and minority labeled data sets. We develop a case study of multi-class anomaly detection in the approach to landing of commercial aircraft in order to benchmark RESAD’s performance to baseline methods. Furthermore, we develop an optimization scheme where the model is optimized to not only reach maximum accuracy, but also a desired interpretability and robustness to adversarial perturbations.

How to realize the effect of Kundalini yoga and Chinese Chi meditation on the HRV and ANS with GAN architecture? ‘HRV-GAN’: An alternative approach

The alternative approach highlighted in our study ameliorates the process of analyzing the effect on cardiac dynamics due to the practice of Chinese Chi meditation and Kundalini yoga by the implementation of ‘HRV-GAN'. It works by augmenting the dataset of Heart Rate Variability (HRV) signals by 31 times with the aid of the Generative Adversarial Network (GAN) and after that analyzing them without the need for any other equivalent conversion of the signal. The study highlights the distinct effects of practicing different types of meditation on HRV by augmenting the initial dataset entirely in an automated deep learning approach. The visualized results from the architecture are verified statistically. The dataset chosen for the study comprised just 24 meditative HRV samples in total, which have been successfully augmented to 744 samples for classification. The results indicated that meditation affects cardiac dynamics significantly, and diversion varies with the choice of meditation technique. The results obtained from the proposed semi-supervised deep learning model- 'HRV-GAN' was verified statistically by plotting required histograms, Recurrence Plots (RP), and Recurrence Quantification Analysis (RQA) analysis, which were in agreement with each other. HRV-GAN has achieved a sensitivity of 96.667% and an accuracy of 95.556 %. Our innovation can benefit the medical and biomedical community for research involving automated analysis of HRV signals because 'HRV-GAN' can perform appreciably even with fewer data samples for training a deep learning model.

Dealing with distribution mismatch in semi-supervised deep learning for COVID-19 detection using chest X-ray images: A novel approach using feature densities

In the context of the global coronavirus pandemic, different deep learning solutions for infected subject detection using chest X-ray images have been proposed. However, deep learning models usually need large labelled datasets to be effective. Semi-supervised deep learning is an attractive alternative, where unlabelled data is leveraged to improve the overall model’s accuracy. However, in real-world usage settings, an unlabelled dataset might present a different distribution than the labelled dataset (i.e. the labelled dataset was sampled from a target clinic and the unlabelled dataset from a source clinic). This results in a distribution mismatch between the unlabelled and labelled datasets. In this work, we assess the impact of the distribution mismatch between the labelled and the unlabelled datasets, for a semi-supervised model trained with chest X-ray images, for COVID-19 detection. Under strong distribution mismatch conditions, we found an accuracy hit of almost 30%, suggesting that the unlabelled dataset distribution has a strong influence in the behaviour of the model. Therefore, we propose a straightforward approach to diminish the impact of such distribution mismatch. Our proposed method uses a density approximation of the feature space. It is built upon the target dataset to filter out the observations in the source unlabelled dataset that might harm the accuracy of the semi-supervised model. It assumes that a small labelled source dataset is available together with a larger source unlabelled dataset. Our proposed method does not require any model training, it is simple and computationally cheap. We compare our proposed method against two popular state of the art out-of-distribution data detectors, which are also cheap and simple to implement. In our tests, our method yielded accuracy gains of up to 32%, when compared to the previous state of the art methods. The good results yielded by our method leads us to argue in favour for a more data-centric approach to improve model’s accuracy. Furthermore, the developed method can be used to measure data effectiveness for semi-supervised deep learning model training.

An active semi-supervised deep learning model for human activity recognition

Human activity recognition (HAR), which aims at inferring the behavioral patterns of people, is a fundamental research problem in digital health and ambient intelligence. The application of machine learning methods in HAR has been investigated vigorously in recent years. However, there are still a number of challenges confronting the task, where one significant barrier lies in the longstanding shortage of annotations. To address this issue, we establish a new paradigm for HAR, which integrates active learning and semi-supervised learning into one framework. The main idea is to reduce the annotation cost by actively selecting the most informative samples for annotation, as well as leveraging the unlabelled instances in a semi-supervised way. In particular, we propose to utilize the massive unlabelled data via temporal ensembling of convolutional neural networks (CNN), which yields robust consensus predictions by aggregating the outputs of the training networks on different epochs. We conducted extensive experiments on three public benchmark datasets. The proposed method achieves Macro F1 values of 0.76, 0.45 and 0.91 in a low annotation scenario on PAMAP2, USCHAD and UCIHAR datasets respectively, outperforming a multitude of state-of-the-art deep models. The ablation study proves the effectiveness of the two components of the framework, i.e., active learning-based sample selection and semi-supervised model training with temporal ensembling, in alleviating the issue of insufficient labels. Cross-validation and statistical significance experiments further demonstrate the robustness and generalization ability of the proposed method. The source codes are available at https://github.com/HaixiaBi1982/ActSemiCNNAct.

Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model

Prediction of building energy performance is a critical strategy for building energy management. Extant studies established city-scale prediction models only based on buildings with energy data. However, building energy data in most cities is limited, which may impair model performance. A large number of unlabeled buildings (without energy data) may reveal important energy use knowledge, but few studies have explored their capability to improve building energy prediction. Therefore, a novel semi-supervised deep learning method, namely dynamically updated multi-fold semi-supervised learning method based on deep neural networks (DUMSL-DNN) is proposed to predict building energy use intensity (EUI) by utilizing unlabeled samples. Manhattan is selected as a case study, which contains 4854 labeled samples and 34,456 unlabeled samples. Compared with the optimal DNN model, DUMSL-DNN can improve building EUI prediction with root-mean-square error (RMSE) reduced by 9.36% and mean absolute error (MAE) reduced by 9.43%. The DUMSL method is superior to typical semi-supervised learning methods with the lowest RMSE of 0.5207 and the lowest MAE of 0.3325. By the implementation of DUMSL-DNN, more areas with high EUI are identified in Manhattan. Specifically, commercial buildings and residential buildings built before 1965 have higher EUI. Measures are accordingly proposed to improve building energy efficiency.

Handling partially labeled network data: A semi-supervised approach using stacked sparse autoencoder

Network traffic analytics has become a crucial task in order to better understand and manage network resources, especially in the network softwarization era where the implementation of this concept can be done easily with network function virtualization. Currently, many approaches have been proposed to improve the performance of traffic classification. However, as new types of traffic emerge every day (and they are generally not labeled), this opens a new challenge to be handled. Moreover, the question of how to accurately classify the traffic using a limited amount of labeled data or partially labeled data is also another important concern. In fact, labeling data is often difficult and time-consuming. In order to solve the previously described issues, we reformulate traffic classification into a semi-supervised learning where both supervised learning (using labeled data) and unsupervised learning (no label data) are combined. To do so, this paper presents a stacked sparse autoencoder (SSAE) based semi-supervised deep-learning model for traffic classification. The main motivations of this approach are: (i) unlabeled data is often abundant and easily available; (ii) classification performance of the whole model can be greatly improved when a large amount of unlabeled traffic is included in the training process; (iii) there is a limit to how much human effort can be thrown at the labeling problem. To investigate the performance of our approach, an empirical study has been conducted on a real dataset and results indicate that using a large amount of unlabeled data in the SSAE pre-trained phase can improve significantly the classification performance of the whole model. Furthermore, the proposed approach is compared against other representative machine-learning and deep-learning models, which are Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), eXtreme Gradient Boosting (XGBoost), and Autoencoder.

Markerless Dog Pose Recognition in the Wild Using ResNet Deep Learning Model

The analysis and perception of behavior has usually been a crucial task for researchers. The goal of this paper is to address the problem of recognition of animal poses, which has numerous applications in zoology, ecology, biology, and entertainment. We propose a methodology to recognize dog poses. The methodology includes the extraction of frames for labeling from videos and deep convolutional neural network (CNN) training for pose recognition. We employ a semi-supervised deep learning model of reinforcement. During training, we used a combination of restricted labeled data and a large amount of unlabeled data. Sequential CNN is also used for feature localization and to find the canine’s motions and posture for spatio-temporal analysis. To detect the canine’s features, we employ image frames to locate the annotations and estimate the dog posture. As a result of this process, we avoid starting from scratch with the feature model and reduce the need for a large dataset. We present the results of experiments on a dataset of more than 5000 images of dogs in different poses. We demonstrated the effectiveness of the proposed methodology for images of canine animals in various poses and behavior. The methodology implemented as a mobile app that can be used for animal tracking.