Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features

Automatic target recognition has been one of the hottest research in synthetic aperture radar (SAR) data processing. Noticing that popular recognition methods cannot utilize multiple features of SAR complex data, a method fused scattering center feature and deep convolutional neural network (CNN) feature is proposed in this letter. This method contains three key parts, namely, scattering center extraction and reconstruction block, CNN feature extraction block, and final feature fusion and classification block. In this process, the scattering center feature and CNN feature are fused at the level of feature maps, which retain the space information of 2-D feature maps. What is more, the proposed half end-to-end strategy realizes the automatic update of weighting parameters in feature extraction network and subnetwork, which promotes a better recognition efficiency. Experimental results on measured SAR data show that the proposed method can achieve better accuracy than other single feature-based methods and feature fusion methods.

We present a method to combine the Fisher vector representation and the Deep Convolutional Neural Network (DCNN) features to generate a rerpesentation, called the Fisher vector encoded DCNN (FV-DCNN) features, for unconstrained face verification. One of the key features of our method is that spatial and appearance information are simultaneously processed when learning the Gaussian mixture model to encode the DCNN features. Evaluations on two challenging verification datasets show that the proposed FV-DCNN method is able to capture the salient local features and also performs well when compared to many state-of-the-art face verification methods.

Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI.

Scene images usually involve semantic correlations, particularly when considering large-scale image data sets. This paper proposes a novel generative image representation, correlated topic vector, to model such semantic correlations. Oriented from the correlated topic model, correlated topic vector intends to naturally utilize the correlations among topics, which are seldom considered in the conventional feature encoding, e.g., Fisher vector, but do exist in scene images. It is expected that the involvement of correlations can increase the discriminative capability of the learned generative model and consequently improve the recognition accuracy. Incorporated with the Fisher kernel method, correlated topic vector inherits the advantages of Fisher vector. The contributions to the topics of visual words have been further employed by incorporating the Fisher kernel framework to indicate the differences among scenes. Combined with the deep convolutional neural network (CNN) features and Gibbs sampling solution, correlated topic vector shows great potential when processing large-scale and complex scene image data sets. Experiments on two scene image data sets demonstrate that correlated topic vector improves significantly the deep CNN features, and outperforms existing Fisher kernel-based features.

Instruments detect and respond to various parameters, playing a crucial role in civil and industrial applications. However, the current instrument recognition algorithm still suffers from low detection accuracy, a large amount of model calculation, and a low degree of automatic recognition. In this article, we propose an efficient real-time automatic instrument indication acquisition algorithm based on a lightweight deep convolutional neural network and hybrid attention fine-grained features. In this algorithm, we divide instrument identification into three subtasks, which are instrument object detection, instrument fine-grained recognition, and instrument indication acquisition. In the instrument object detection task, we propose a lightweight instrument detection algorithm, which uses a lightweight feature extraction backbone network and a single-scale object detection algorithm to reduce the computational load. In the instrument fine-grained recognition task, this article proposes a fine-grained classification network based on attention hybrid clipping, which can effectively recognize the instrument fine-grained category and range features. In the instrument indication acquisition task, the edge detection and polar coordinate change are used to realize an automated instrument identification and reading system, which can meet the needs of high-precision real-time monitoring of the instrument indication in various complex scenarios. In addition, we have created and made publicly available an instrument image dataset that contains 3457 images of various types of instruments in actual scenes. The recognition accuracy of the instrument reading recognition algorithm proposed in this article is 92.9% on the created dataset, and the average recognition time per image is 348 ms on the i7-8700 CPU.

Scene classification of high resolution remote sensing images plays an important role for a wide range of applications. While significant efforts have been made in developing various methods for scene classification, most of them are based on handcrafted or shallow learning-based features. In this paper, we investigate the use of deep convolutional neural network (CNN) for scene classification. To this end, we first adopt two simple and effective strategies to extract CNN features: (1) using pre-trained CNN models as universal feature extractors, and (2) domain-specifically fine-tuning pre-trained CNN models on our scene classification dataset. Then, scene classification is carried out by using simple classifiers such as linear support vector machine (SVM). In our work, three off-the-shelf CNN models including AlexNet [1], VGGNet [2], and GoogleNet [3] are investigated. Comprehensive evaluations on a publicly available 21 classes land use dataset and comparisons with several state-of-the-art approaches demonstrate that deep CNN features are effective for scene classification of high resolution remote sensing images.

Indoor scene recognition is complex due to the commonality shared between different spaces. Still, when it comes to robotics applications, the uncertainty increases due to illumination change, motion blur, interruption due to external light sources, and cluttered environments. Most existing fusion approaches do not consider the uncertainty, and others have a high computational cost that may not suit robots with limited resources. To mitigate these issues, this paper proposes a reliable indoor scene recognition approach for mobile robots with limited resources based on robust deep convolutional neural networks (CNNs) feature extractors and neuro-fuzzy inference to consider the uncertainty of the data. All CNN feature extractors are pre-trained on the Imagenet dataset and used in the manner of transfer learning. The performance of our fusion method has been assessed on a customized MIT-67 dataset and for real-time processing on a Locobot robot. We also compare the proposed method with two standard fusion methods---Early Feature Fusion (EFF) and Weighted Average Late Fusion (WALF). The experimental results demonstrate that our method achieves competitive results with a precision of 94%, and it performs well on the Locobot robot with a speed of 3.1 frames per second.

Fusing learned representations from Riesz Filters and Deep CNN for lung tissue classification.

Hepatic cystic echinococcosis (HCE), a life-threatening liver disease, has 5 subtypes, i.e., single-cystic, polycystic, internal capsule collapse, solid mass, and calcified subtypes. And each subtype has different treatment methods. An accurate diagnosis is the prerequisite for effective HCE treatment. However, clinicians with less diagnostic experience often make misdiagnoses of HCE and confuse its 5 subtypes in clinical practice. Computer-aided diagnosis (CAD) techniques can help clinicians to improve their diagnostic performance. This paper aims to propose an efficient CAD system that automatically differentiates 5 subtypes of HCE from the ultrasound images. The proposed CAD system adopts the concept of deep transfer learning and uses a pre-trained convolutional neural network (CNN) named VGG19 to extract deep CNN features from the ultrasound images. The proven classifier models, k - nearest neighbor (KNN) and support vecter machine (SVM) models, are integrated to classify the extracted deep CNN features. 3 distinct experiments with the same deep CNN features but different classifier models (softmax, KNN, SVM) are performed. The experiments followed 10 runs of the five-fold cross-validation process on a total of 1820 ultrasound images and the results were compared using Wilcoxon signed-rank test. The overall classification accuracy from low to high was 90.46 ± 1.59% for KNN classifier, 90.92 ± 2.49% for transfer learned VGG19, and 92.01 ± 1.48% for SVM, indicating SVM classifiers with deep CNN features achieved the best performance (P < 0.05). Other performance measures used in the study are specificity, sensitivity, precision, F1-score, and area under the curve (AUC). In addition, the paper addresses a practical aspect by evaluating the system with smaller training data to demonstrate the capability of the proposed classification system. The observations of the study imply that transfer learning is a useful technique when the availability of medical images is limited. The proposed classification system by using deep CNN features and SVM classifier is potentially helpful for clinicians to improve their HCE diagnostic performance in clinical practice.

Interstitial lung disease (ILD) causes pulmonary fibrosis. The correct classification of ILD plays a crucial role in the diagnosis and treatment process. In this research work, we propose a lung nodules recognition method based on a deep convolutional neural network (DCNN) and global features, which can be used for computer-aided diagnosis (CAD) of global features of lung nodules. Firstly, a DCNN is constructed based on the characteristics and complexity of lung computerized tomography (CT) images. Then we discussed the effects of different iterations on the recognition results and influence of different model structures on the global features of lung nodules. We also incorporated the improvement of convolution kernel size, feature dimension, and network depth. Thirdly, the effects of different pooling methods, activation functions and training algorithms we proposed has been analyzed to demonstrate the advantages of the new strategy. Finally, the experimental results verify the feasibility of the proposed DCNN for CAD of global features of lung nodules, and the evaluation shown that our proposed method could achieve an outstanding results compare to state-of-arts.

The accurate separation of ECG signals has become crucial to identify heart diseases. Machine learning methods are widely used to separate ECG signals. The aim of this study was to obtain optimal number of hidden neurons of the Extreme Learning Machine (ELM) using the differential evolution algorithm (DEA) and increase the accuracy rate of ECG classification. In this study, a public database on PhysioNet was used for ECG signal classification. A deep feature method using convolutional neural network was used to extract the major features of the ECG samples. Then, a conventional ELM was applied to the ECG signals. Subsequently, the ECG signals with deep properties were shared with the MATLAB classifier toolbox (k-NN, SVM, Decision Trees). In addition, the ECG signals in the dataset were tested using the Genetic Algorithm Wavelet Kernel-ELM (GAWK-ELM). Finally, the DEA-ELM was improved for the determination of the number of hidden neurons. This study optimized the hidden neuron numbers of traditional ELM with DEA using deep learning capabilities in the feature extraction. The aim of was to maximize the best cost of the DEA and achieve the optimal number of hidden neurons in ELM. Accuracy (Acc), sensitivity (Se), specificity (Spe) and F-measure were used as the performance metrics for the classifier performances. The classification results were 80.60%, 81.50%, and 83.12% with SVM, ELM and DEA-ELM, respectively. Thus, the best classification scores were accomplished with an accuracy of 83.12% with the algorithm supported by the DEA.

The main work of human motion gesture recognition is to recognize and analyze the behavior of human objects in the video. Although the current research in the field of human motion gesture recognition has achieved certain results, the human motion gesture recognition in real life scenes has great effects due to factors such as camera movement, target scale transformation, dynamic background, viewing angle, and illumination. This article first proposes a new method of constructing human motion posture features to describe human behavior. This method is based on deep convolutional neural network features and topic models. Experiments have verified that compared with the traditional feature map extracted from the convolutional neural network fully connected layer, the feature map extracted from the convolutional neural network convolutional layer is not only lower in dimension but also has higher discrimination. Secondly, based on the feature map of the convolutional neural network, the training map downsampling strategy is used to overcome the interference caused by the object's scale change and shape change. Finally, based on the basketball gesture recognition method, the behavior performance of the legs and arms in 9 basketball actions of walking, running, jumping, standing dribbling, walking dribbling, running dribbling, shooting, passing and receiving is analyzed. As well as the corresponding signal waveform characteristics, a two-stage data division method for basketball is proposed. The unit action data is extracted for analysis to realize feature extraction. In order to select the most suitable classifier for basketball gesture recognition, the constructed feature vector uses four Different classifiers are trained to construct different classifiers to realize the division of actions.

Occluded facial expression recognition (OFER) poses a formidable challenge in real-world applications, particularly in human-computer interaction and affective computing. Despite recent advancements, existing methodologies often struggle to maintain optimal accuracy under occlusion constraints. This study proposes a novel hybrid framework that synergizes handcrafted and deep learning-based features to enhance robustness and precision in emotion recognition. Specifically, we integrate Histogram of Oriented Gradients (HoG), facial landmark descriptors, and sliding window-based HoG representations with deep convolutional neural network (CNN) features, leveraging their complementary strengths. Our experimental design explores multiple feature fusion strategies, including CNN-based automated classification and a hybrid model incorporating Dlib-extracted landmarks with HoG-CNN integration. Comparative analysis against state-of-the-art approaches demonstrates that our multi-feature fusion technique significantly improves recognition accuracy, achieving a remarkable 96% accuracy on benchmark datasets such as RAF-DB and AffectNet. However, we observe a marginal decline in performance with increased dataset complexity, emphasizing the need for scalable solutions. This research underscores the efficacy of integrating handcrafted and deep learning-driven features, offering a promising direction for advancing occlusion-robust facial expression recognition in dynamic environments.

G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition

Image classification is getting more attention in the area of computer vision. During the past few years, a lot of research has been done on image classification using classical machine learning and deep learning techniques. Presently, deep learning-based techniques have given stupendous results. The performance of a classification system depends on the quality of features extracted from an image. The better is the quality of extracted features, the more the accuracy will be. Although, numerous deep learning-based methods have shown enormous performance in image classification, still due to various challenges deep learning methods are not able to extract all the important information from the image. This results in a reduction in overall classification accuracy. The goal of the present research is to improve the image classification performance by combining the deep features extracted using popular deep convolutional neural network, VGG19, and various handcrafted feature extraction methods, i.e., SIFT, SURF, ORB, and Shi-Tomasi corner detector algorithm. Further, the extracted features from these methods are classified using various machine learning classification methods, i.e., Gaussian Naïve Bayes, Decision Tree, Random Forest, and eXtreme Gradient Boosting (XGBClassifier) classifier. The experiment is carried out on a benchmark dataset Caltech-101. The experimental results indicate that Random Forest using the combined features give 93.73% accuracy and outperforms other classifiers and methods proposed by other authors. The paper concludes that a single feature extractor whether shallow or deep is not enough to achieve satisfactory results. So, a combined approach using deep learning features and traditional handcrafted features is better for image classification.