Large-scale Image Recognition Research Articles

Memristive Explainable Artificial Intelligence Hardware.

Artificial intelligence (AI) is often considered a black box because it provides optimal answers without clear insight into its decision-making process. To address this black box problem, explainable artificial intelligence (XAI) has emerged, which provides an explanation and interpretation of its decisions, thereby promoting the trustworthiness of AI systems. Here, a memristive XAI hardware framework is presented. This framework incorporates three distinct types of memristors (Mott memristor, valence change memristor, and charge trap memristor), each responsible for performing three essential functions (perturbation, analog multiplication, and integration) required for the XAI hardware implementation. Three memristor arrays with high robustness are fabricated and the image recognition of 3 × 3 testing patterns and their explanation map generation are experimentally demonstrated. Then, a software-based extended system based on the characteristics of this hardware is built, simulating a large-scale image recognition task. The proposed system can perform the XAI operations with only 4.32% of the energy compared to conventional digital systems, enlightening its strong potential for the XAI accelerator.

Deep learning based data augmentation for large-scale mineral image recognition and classification

Vision-based mineral image recognition and classification is a proven solution for autonomous unmanned ore sorting. Although accurate identification can be achieved by training models offline using large-scale datasets, the lack of sufficient labeled images still limits the accessibility and exploration of high-performance deep learning models. To address the above issues, referring to the generative adversarial networks, three different deep learning-based mineral image data augmentation models are proposed in this work. The experimental results show that the proposed models can generate mineral images with high fidelity and have high similarity to the ground truth in terms of texture, color and shape. Compared with classic data augmentation methods, proposed ones can better optimize downstream sorting tasks: the accuracy of ResNet101, ResNet50, InceptionV3 and VGG19 is improved by 18.52%, 9.94%, 4.39% and 2.39%, respectively. Finally, this work also presents a monolithic three-stage system workflow for large-scale mineral image recognition and classification.

Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem

<p>In this study, artificial neural networks (ANNs) are being used to diagnose hair loss in patients. An autoimmune condition known as Alopecia Areata (AA) results in hair loss in the affected area. The most recent figures from throughout the world show that AA affects 1 in 1000 persons and has a 2% incidence rate. Based on the look of photographs with healthy hair in the dataset, machine learning techniques were employed to classify the conditions. Before making predictions, each of these ANNs algorithms creates a prediction model using pictures of healthy hair. The aim of this study is to evaluate the accuracy of neural networks for alopecia detection in human subjects. The study presents a classification framework for distinguishing between healthy hairs (HHs) and Alopecia Areata (AA). The framework incorporates Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement and segmentation techniques to enhance the quality of the images. Additionally, Data Augmentation (DA) is employed to generate additional data and improve the precision of the proposed framework. To extract features from the images, two powerful techniques are utilized. The Visual Geometry Group (VGG), which consists of very deep convolutional networks designed for large-scale image recognition, is employed. VGG networks have proven to be effective in learning complex features directly from data, eliminating the need for manual feature extraction. Additionally, a Convolutional Neural Network (CNN), a deep learning network architecture specifically designed for image processing tasks, is employed. To create a machine learning model for classification, the Support Vector Machine (SVM) approach is utilized. SVM is a widely used algorithm in supervised learning, capable of solving both classification and regression problems. Its versatility and effectiveness make it a suitable choice for the classification task in this study. By combining the CLAHE enhancement, segmentation, data augmentation, feature extraction using VGG and CNN, and classification using SVM, the proposed framework aims to accurately classify HHs and AA cases.</p>

Knowledge driven weights estimation for large-scale few-shot image recognition

We study the topic of large-scale few-shot image recognition with semantic-visual relational knowledge-based transfer learning. Compared with classical few-shot learning, which is defined as a k-way (k denotes the number of categories, usually 5/10-way) classification problem, large-scale few-shot recognition contains more categories (100-way +) with few samples per category and is easier to overfit. A promising direction of large-scale few-shot learning is transferring prior relevant semantic/visual knowledge from outside data to accelerate the convergence on limited positives. Inspired by this, we propose a novel Knowledge Driven Weights Estimation framework. Specifically, the framework leverages semantic and visual relations between new few-shot and existed many-shot categories to transfer knowledge trained on many-shot datasets (e.g., ImageNet-1000). We show that the transferred knowledge provides a good initialization for novel few-shot categories leading to faster convergence speed and higher performance than random/imprinting initialization. Experimental results on additional un-seen ImageNet categories (other than the 1000 categories) with few positives show that our method is effective on large-scale few-shot recognition.

DL_Track - Automated analysis of muscle architecture from B-mode ultrasonography images using deep learning

B-mode ultrasound is commonly used to image musculoskeletal tissues, but one major bottleneck is data analysis. Manual analysis is commonly deployed for assessment of muscle thickness, pennation angle and fascicle length in muscle ultrasonography images. However, manual analysis is somewhat subjective, laborious and requires thorough experience. We provide an openly available algorithm (DL_Track) to automatically analyze muscle architectural parameters in ultrasonography images or videos of human lower limb muscles.
 We trained two different neural networks (classic U-net [Ronneberger et al., 2021] and U-net with VGG16 [Simonyan & Zisserman, 2015] pretrained encoder) one to detect muscle fascicles and another to detect muscle aponeuroses using a set of labelled musculoskeletal ultrasound images. We included images from four different devices of the vastus lateralis, gastrocnemius medialis, tibilias anterior and soleus. In total, we included 310 images for the fascicle model and 570 images for the aponeuroses model, which we augmented to about 1,700 images per set. Each dataset was randomly split into a training and test set for model training, using a common 80/20 train/test split. We determined the best performing model based on intersection-over-union and loss metrics calculated during model training. We compared neural network predictions on an unseen test set consisting of 35 images to those obtained via manual analysis and two existing semi/automated analysis approaches (SMA and Ultratrack).
 Across the set of 35 unseen images, the mean differences between DL_Track and manual analysis were for fascicle length -2.4 mm (95% compatibility interval (CI) = -3.7 to -1.2), for pennation angle 0.6° (-0.2 to 1.4), and for muscle thickness -0.6 mm (-1.2 to 0.002). The corresponding values comparing DL_Track with SMA were for fascicle length 5.2 mm (1.3 to 9.0), for pennation angle -1.4° (-2.6 to -0.4) and for muscle thickness -0.9 mm (-1.5 to -0.3) respectively. ICC values between DL_Track and Ultratrack were 0.19 (0.00 to 0.35) for medial gastrocnemius passive contraction, 0.79 (0.77 to 0.81) for medial gastrocnemius maximal voluntary contraction, 0.88 (0.87 to 0.89) for calf raise, 0.67 (0.07 to 0.86) for medial gastrocnemius during walking, 0.80 (0.79 to 0.82) for tibialis passive plantar and dorsiflexion, and 0.85 (0.83 to 0.86) for tibialis anterior maximum voluntary contraction.
 Our method is fully automated and can estimate fascicle length, pennation angle and muscle thickness from single images or videos in multiple superficial muscles. For single images, the method gave results that are in agreement with those produced by SMA or manual analysis. Similarly, for videos, there was overlap between the results produced with Ultratrack and our method. In contrast to Ultratrack, DL_Track analyzes each frame independently of the previous frames, which might explain the observerd variability.
 References
 Ronneberger, O., Fischer, P., & Brox, T. (2021). U-Net: Convolutional networks for biomedical image segmentation. arXiv. https://doi.org/10.48550/arXiv.1505.04597
 Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv. https://doi.org/10.48550/arXiv.1409.1556

Depth-wise Decomposition for Accelerating Separable Convolutions in Efficient Convolutional Neural Networks

Very deep convolutional neural networks (CNNs) have been firmly established as the primary methods for many computer vision tasks. However, most state-of-the-art CNNs are large, which results in high inference latency. Depth-wise separable convolution has been proposed for image recognition tasks on platforms with limited computation power, such as robots and self-driving cars. Any regular deep CNN has a depth-wise separable counterpart, which is faster, but less accurate, when equally trained. In this paper, we propose a novel decomposition approach based on SVD, namely depth-wise decomposition, for converting regular convolutions into depth-wise separable convolutions post-training, while maintaining high accuracy. We show that our approach generalizes to the multi-channel and multi-layer cases, by applying Generalized Singular Value Decomposition (GSVD). We conduct thorough experiments with the ShuffleNet V2 model on a large-scale image recognition dataset: ImageNet. Our approach outperforms the baseline, channel decomposition. Moreover, our approach improves the Top-1 accuracy of ShuffleNet V2 by ∼2%.

A CNN and VGG- 16 Approach for Identification System Based on Ear Biometrics

cAbstract: With the increase in digitization over the entire world, there is a growing concern for security and privacy. Alphonse Bertillon, who created a technique to identify a person by precise body measures, is credited with developing biometrics for the first time in Paris during the 1800s. Since then, biometrics have greatly aided in preserving human security and privacy. The proposed study offers a through investigation of the ear as a biometric system utilising a novel taxonomy. It also provides a detailed analysis of databases, performance evaluation criteria, and cutting-edge methodologies. This research work acquired 99.8% accuracy and 0.2% loss while training and testing the model. Convolution Neural Network (CNN) is used due to its proven accuracy in object detection. To achieve better results in large scale image recognition. The research has been supported with the datasets from Kaggle. As a benchmark for comparison, the database from IIT Delhi. The results show an enhancement over the existing techniques and act as a springboard for new ear biometrics researchers.

Deep Residual Vector Encoding for Vein Recognition

Vein recognition has been drawing more attention recently because it is highly secure and reliable for practical biometric applications. However, underlying issues such as uneven illumination, low contrast, and sparse patterns with high inter-class similarities make the traditional vein recognition systems based on hand-engineered features unreliable. Recent successes of convolutional neural networks (CNNs) for large-scale image recognition tasks motivate us to replace the traditional hand-engineered features with the superior CNN to design a robust and discriminative vein recognition system. To address the difficulty of direct training or fine-tuning of a CNN with existing small-scale vein databases, a new knowledge transfer approach is formulated using pre-trained CNN models together with a training dataset (e.g., ImageNet) as a robust descriptor generation machine. With the generated deep residual descriptors, a very discriminative model, namely deep residual vector encoding (DRVE), is proposed by a hierarchical design of dictionary learning, coding, and classifier training procedures. Rigorous experiments are conducted with a high-quality hand-dorsa vein database, and superior recognition results compared with state-of-the-art models fully demonstrate the effectiveness of the proposed models. An additional experiment with the PolyU multispectral palmprint database is designed to illustrate the generalization ability.

Gender recognition of Guanyin in China based on VGGNet

Gender transformation of Guanyin (Avalokitesvara in India) in China is an intrinsically fascinating research topic. Besides the inner source from the scriptures, literatures and epigraphs, iconological analysis is usually as the external evidence of Guanyin’s gender recognition. However, the ambiguous gender of the Guanyin image is often intentional and can be objectively assessed. Can computer vision be applied to the recognition objectively and quantitatively? In this paper, VGGNet (VGGNet is a very deep convolutional network for large-scale image recognition proposed by Visual Geometry Group of Oxford University) is applied to propose an automatic gender recognition system. To validate its efficiency, abundant experiments are implemented on the images of Dazu Rock Carvings, Dunhuang Mogao Caves, and Yungang Grottoes. The following conclusions can be made according to the quantitative results. Firstly, VGG-based method can be effectively applied to the gender recognition on non-Buddhist and Buddhist images. Compared with five classical feature extraction methods, VGG-based method performs not much better on non-Buddhist images, but superior on Buddhist images. Furthermore, the experiments are also carried out on three different training datasets, real-world facial datasets, including CUHK (CUHK is a student face database of Chinese University of Hong Kong). IMFDB (IMFDB is an Indian movie face database.) and CAS-PEAL (CAS-PEAL is a Chinese face database created by Chinese Academy of Sciences (CAS) with varying pose, expression, accessory, and lighting (PEAL). The unsatisfactory results based on IMFDB indicate that it is not valid to apply Indian facial images as a training set to the gender recognition on Buddhist image in China. With the sinicization of Buddhism, there were more Chinese rather than Indian characteristics on Buddhist images in ancient China. The results based on CAS-PEAL are more robust than those based on CUHK, as the former is mainly composed of mature adult faces, and the latter consists of young student faces. It gives the evidence that Buddha and Bodhisattva (Guanyin included) were as ideally mature men in original Buddhist art. The last but the most meaningful is that besides the time factor, the relationship between image making and the scriptures, or the intentional combination of male and female features, the geographical impact should not be ignored when we talk about the gender transformation of Guanyin in ancient China. The gender of Guanyin frescoes in Dunhuang Mogao Caves painted in the Sui, Tang, Five, Song and Yuan dynasties were always with prominent male characteristics (with tadpole-like moustache), while bodhisattvas in Yungang Grottoes engraved in the Northern Wei Dynasty were feminine even though they were made earlier than those in Dunhuang Mogao Caves. It is quite different from the common idea that the feminization of Guanyin occurred in the early Tang Dynasties and completely feminized in the late Tang Dynasty. Both the quantitative results and image analysis indicate that there might be a common model in a specific region, so the image-making of Guanyin was affected much more by geographical rather than temporal factor. In a word, it is quite a complicated issue for the feminization of Guanyin in China.

XNOR-Nets with SETs: Proposal for a binarised convolution processing elements with Single-Electron Transistors

Deep neural network (DNN) and Convolution neural network (CNN) algorithms have significantly increased the accuracies in cutting-edge large-scale image recognition and natural-language processing tasks. Generally, such neural nets are implemented on power-hungry GPUs, beyond the reach of low-power edge-devices. The binary neural nets have been proposed recently, where both the input activations and weights are constrained to + 1 and − 1 to address this challenge. Here in the present proof-of-concept study, we propose a simple class of mixed-signal circuits composed of single-electron devices and exploit the nonlinear Coulomb staircase phenomena to alleviate the challenges of binarised deep learning hardware accelerators. In particular, through SPICE modeling, we demonstrate the realisation of space-time-energy efficient XNOR-Accumulation (XAC) operation, reconfigurabilty of XAC circuit to perform 1D convolution and a busbar design to augment a contemporary accelerator. These nanoscale circuits could be readily fabricated and may potentially be deployed in low-power deep-learning systems.

4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection.

Due to the high availability of large-scale annotated image datasets, knowledge transfer from pretrained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domain. In this article, we propose a novel deep convolutional neural network, which we called self-supervised super sample decomposition for transfer learning (4S-DT) model. The 4S-DT encourages a coarse-to-fine transfer learning from large-scale image recognition tasks to a specific chest X-ray image classification task using a generic self-supervised sample decomposition approach. Our main contribution is a novel self-supervised learning mechanism guided by a super sample decomposition of unlabeled chest X-ray images. 4S-DT helps in improving the robustness of knowledge transformation via a downstream learning strategy with a class-decomposition (CD) layer to simplify the local structure of the data. The 4S-DT can deal with any irregularities in the image dataset by investigating its class boundaries using a downstream CD mechanism. We used 50000 unlabeled chest X-ray images to achieve our coarse-to-fine transfer learning with an application to COVID-19 detection, as an exemplar. The 4S-DT has achieved a high accuracy of 99.8% on the larger of the two datasets used in the experimental study and an accuracy of 97.54% on the smaller dataset, which was enriched by augmented images, out of which all real COVID-19 cases were detected.

Multi-scale information fusion model for feature extraction of converter transformer vibration signal

The vibration signal feature extraction model of the converter transformer has the problem of low accuracy. The main reason is that the time-related information is easily lost during training. In recent years, convolutional neural networks (CNN) have been proven to break the limits of traditional classification models. CNN has achieved remarkable results in large-scale image recognition tasks. To solve the problem of difficulty in model building and loss of time-related information, this paper proposes a multi-scale fusion feature extraction model for converter transformer vibration signal. The model includes an image generation module and a multi-scale information fusion module. The image generation module converts vibration signals into time-domain, frequency-domain, and energy feature images by calculating the Markov Transform Field and Continuous Wavelet Transform. In the multi-scale information fusion module, the attention module is introduced into the multi-parallel convolutional neural network to fuse the feature images. The proposed method can make full use of the advantages of deep learning through generating images that can represent time series from multi-scale. To evaluate the effectiveness of the model in this paper, we establish a data set based on the measured vibration signals and tested by classification experiments. The result shows the accuracy of the model state recognition in this paper is 96.15%, which is better than classic time series processing networks such as LSTM and 1D-CNN.

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM.

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

Machine Learning Analysis of Multispectral Imaging and Clinical Risk Factors to Predict Amputation Wound Healing

Machine Learning Analysis of Multispectral Imaging and Clinical Risk Factors to Predict Amputation Wound Healing

Automatic Identification of Individual Primates with Deep Learning Techniques.

SummaryThe difficulty of obtaining reliable individual identification of animals has limited researcher's ability to obtain quantitative data to address important ecological, behavioral, and conservation questions. Traditional marking methods placed animals at undue risk. Machine learning approaches for identifying species through analysis of animal images has been proved to be successful. But for many questions, there needs a tool to identify not only species but also individuals. Here, we introduce a system developed specifically for automated face detection and individual identification with deep learning methods using both videos and still-framed images that can be reliably used for multiple species. The system was trained and tested with a dataset containing 102,399 images of 1,040 individuals across 41 primate species whose individual identity was known and 6,562 images of 91 individuals across four carnivore species. For primates, the system correctly identified individuals 94.1% of the time and could process 31 facial images per second.

A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection

With an overwhelming increase in the demand of autonomous systems, especially in the applications related to intelligent robotics and visual surveillance, come stringent accuracy requirements for complex object recognition. A system that maintains its performance against a change in the object’s nature is said to be sustainable and it has become a major area of research for the computer vision research community in the past few years. In this work, we present a sustainable deep learning architecture, which utilizes multi-layer deep features fusion and selection, for accurate object classification. The proposed approach comprises three steps: (1) By utilizing two deep learning architectures, Very Deep Convolutional Networks for Large-Scale Image Recognition and Inception V3, it extracts features based on transfer learning, (2) Fusion of all the extracted feature vectors is performed by means of a parallel maximum covariance approach, and (3) The best features are selected using Multi Logistic Regression controlled Entropy-Variances method. For verification of the robust selected features, the Ensemble Learning method named Subspace Discriminant Analysis is utilized as a fitness function. The experimental process is conducted using four publicly available datasets, including Caltech-101, Birds database, Butterflies database and CIFAR-100, and a ten-fold validation process which yields the best accuracies of 95.5%, 100%, 98%, and 68.80% for the datasets respectively. Based on the detailed statistical analysis and comparison with the existing methods, the proposed selection method gives significantly more accuracy. Moreover, the computational time of the proposed selection method is better for real-time implementation.

An automatic pulmonary nodules detection algorithm with multi-scale information fusion

Lung nodules are the main manifestation of early lung cancer. So accurate detection of lung nodules is of great significance for early diagnosis and treatment of lung cancer. However, the rapid and accurate detection of pulmonary nodules is a challenging task due to the complex background, large detection range of pulmonary computed tomography (CT) images and the different sizes and shapes of pulmonary nodules. Therefore, this paper proposes a multi-scale feature fusion algorithm for the automatic detection of pulmonary nodules to achieve accurate detection of pulmonary nodules. Firstly, a three-layer modular lung nodule detection model was designed on the deep convolutional network (VGG16) for large-scale image recognition. The first-tier module of the network is used to extract the features of pulmonary nodules in CT images and roughly estimate the location of pulmonary nodules. Then the second-tier module of the network is used to fuse multi-scale image features to further enhance the details of pulmonary nodules. The third-tier module of the network was fused to analyze the features of the first-tier and the second-tier module of the network, and the candidate box of pulmonary nodules in multi-scale was obtained. Finally, the candidate box of pulmonary nodules under multi-scale was analyzed with the method of non-maximum suppression, and the final location of pulmonary nodules was obtained. The algorithm is validated by the data of pulmonary nodules on LIDC-IDRI common data set. The average detection accuracy is 90.9%.

Multi-task cascade deep convolutional neural networks for large-scale commodity recognition

In recent years, deep convolutional neural network have achieved remarkable performance in object detection and image classification. However, there are still some practical challenges in large-scale image recognition tasks. To be specific, the visual separability between different object categories is extremely uneven, and some categories have strong inter-class similarities. Existing CNN networks are trained as flat n-way classifiers, which is usually not sufficient to meet the challenges. Hence, we propose a framework: multi-task cascade deep convolutional neural network (MTCD-CNN), which contains two phases: object detection and hierarchical image classification, for large-scale commodity recognition. First, the object detection framework is utilized to locate and crop the areas that may contain objects. Then, hierarchical spectrum clustering is adopted to construct a category and a tree-like image classification model. During the testing phase, the indistinguishable objects are classified from coarse to fine by searching the path of the category tree. The proposed hierarchical image classification method provides an insight into the data by identifying the group of classes that are hard to classify and require more attention when compared to others. Through extensive experiments and comparative analyses of commodity detection in supermarkets and stores of Jinzhou city, the performance of MTCD-CNN has proved to be superior to other advanced methods, indicating that our proposed method has effectively solved the problem of excessive similarity of confusingly similar categories.

A Fast Hybrid Model for Large–Scale Zero–Shot Image Recognition Based on Knowledge Graphs

Zero-shot learning aims to recognize unseen categories by learning an embedding space between data samples and semantic representations. For the large-scale datasets with thousands of categories, embedding vectors of category labels are often used for semantic representation since it is difficult to define the semantic attributes of categories manually. Facing the problem of underutilization of prior knowledge during the construction of embedding vectors, this paper first constructs a novel knowledge graph as the supplement to the basic WordNet graph, and then proposes a fast hybrid model ARGCN-DKG, which means Attention based Residual Graph Convolutional Network on Different types of Knowledge Graphs. By introducing residual mechanism and attention mechanism, and integrating different knowledge graphs, the accuracy of knowledge transfer between different categories can be improved. Our model only use 2-layer GCN, the pretrained image features and category semantic features, so the training process could be done in minitues on single GPU, which could be one of the fastest training models for large-scale image recognition. Experiment results demonstrate that ARGCN-DKG model could get better results for large-scale datasets than the state-of-the-art model.

DeltaFrame-BP: An Algorithm Using Frame Difference for Deep Convolutional Neural Networks Training and Inference on Video Data

Inspired by the success of deep convolutional neural networks (CNNs) with back-propagation (BP) training on large-scale image recognition tasks, recent research efforts concentrated on expending deep CNNs toward more challenging automatized video analysis, such as video classification, object tracking, action recognition and optical flow detection. Video comprises a sequence of images (frames) captured over time in which image data is a function of space and time. Extracting three-dimensional spatial-temporal features from multiple frames becomes a key ingredient for capturing and incorporating appearance and dynamic representations using deep CNNs. Hence, training deep CNNs on video involves significant computational resources and energy consumption due to extended number of frames across the time line of video length. We propose DeltaFrame-BP, a deep learning algorithm, which significantly reduces computational cost and energy consumption without accuracy degradation by streaming frame differences for deep CNNs training and inference. The inherent similarity between video frames due to high fps (frames per second) in video recording helps achieving high-sparsity and low-dynamic range data streaming using frame differences in comparison with raw video frames. According to our simulation, nearly 25 percent energy reduction was achieved in training using the proposed accuracy-lossless DeltaFrame-BP algorithm in comparison with the standard Back-propagation algorithm.