Different Categories Of Images Research Articles

FACSNet: Forensics aided content selection network for heterogeneous image steganalysis

The main goal of image steganalysis, as a technique of confrontation with steganography, is to determine the presence or absence of secret information in conjunction with the specific statistical characteristics of the carrier. With the development of deep learning technology in recent years, the performance of steganography has been gradually enhanced. Especially for the complex reality environment, the image content is mixed and heterogeneous, which brings great challenges to the practical application of image steganalysis technology. In order to solve this problem, we design a forensics aided content selection network (FACSNet) for heterogeneous image steganalysis. Considering the heterogeneous situation of real images, a forensics aided module is introduced to pre-categorise the images to be tested, so that the network is able to detect different categories of images in a more targeted way. The complexity of the images is also further analysed and classified using the content selection module to train a more adapted steganalyser. By doing this, the network is allowed to achieve better performance in recognising and classifying the heterogeneous images for detection. Experimental results show that our designed FACSNet is able to achieve excellent detection performance in heterogeneous environments, improving the detection accuracy by up to 7.14% points, with certain robustness and practicality.

Suitable-Matching Areas’ Selection Method Based on Multi-Level Saliency

Scene-matching navigation is one of the essential technologies for achieving precise navigation in satellite-denied environments. Selecting suitable-matching areas is crucial for planning trajectory and reducing yaw. Most traditional selection methods of suitable-matching areas use hierarchical screening based on multiple feature indicators. However, these methods rarely consider the interrelationship between different feature indicators and use the same set of screening thresholds for different categories of images, which has poor versatility and can easily cause mis-selection and omission. To solve this problem, a suitable-matching areas’ selection method based on multi-level saliency is proposed. The matching performance score is obtained by fusing several segmentation levels’ salient feature extraction results and performing weighted calculations with the sub-image edge density. Compared with the hierarchical screening methods, the matching performance of the candidate areas selected by our algorithm is at least 22.2% higher, and it also has a better matching ability in different scene categories. In addition, the number of missed and wrong selections is significantly reduced. The average matching accuracy of the top three areas selected by our method reached 0.8549, 0.7993, and 0.7803, respectively, under the verification of multiple matching algorithms. Experimental results show this paper’s suitable-matching areas’ selection method is more robust.

A Black Box Comparison of Machine Learning Reverse Image Search for Cybersecurity OSINT Applications

Machine learning algorithms for reverse image search (a subset of open source intelligence or OSINT) provide a free, useful tool for determining the content of an image, where and when it was captured, and, in some cases, whether it has been digitally modified. Using a test data set of 24 images, we compared the performance of reverse image search for Google, Bing, and Yandex. Our black box experimental results are presented for three different categories of images (uncluttered images, images with significant background clutter, and facial recognition). The total number of correct images was highest for Google (65%), while Bing (55%) and Yandex (50%) yielded different results. Google was also the best at identifying cluttered, uncluttered, and facial images. We compare these results with previous studies and review how relative performance has changed over time. Accurate recognition rates for all reverse search platforms tested were higher for original images not previously uploaded, as opposed to images used in earlier studies. We validate our results using exchangeable image file format (EXIF) data and error level analysis (ELA) of selected images. Based on these results, best practices for OSINT image investigation are proposed.

EmoEye: Eye-Tracking and Biometrics Database for Emotion Recognition

Emotion recognition using Machine Learning algorithms is often used both in science and commerce. Responding to the demand for deep learning techniques of automatic emotion detection using biological signals and our own business needs as a neuromarketing laboratory, we created a large dataset of eye tracking and biometrics data suitable for emotion recognition tasks. The EmoEye database sample consisted of 200 people (147 women, 49 men, 4 non-binary individuals; 27.46 ± 11.45 years old). Each respondent was asked to view 316 images from the Open Affective Standardized Image Set (OASIS) and rate them on arousal and valence scales from the Self-Assessment Manikin questionnaire. Eye tracking, galvanic skin response (GSR), and photoplethysmogram were recorded throughout the experiment. Demographic data was also collected for each respondent. The image ratings on the valence scale did not differ statistically from the standard ratings of the corresponding images for the original stimulus base. The overall distribution trends of ratings on both scales for different categories of images were similar for standard ratings and ratings obtained from our respondents. As a result of this study, a corpus of GSR, heart rate variability and eye movement reactions data (fixation coordinates; fixation duration; average pupil size for the right and left eye) was compiled and successfully trained on a multimodal neural network algorithm within our laboratory and is ready for further implementation.

Identifying Laryngeal Neoplasms in Laryngoscope Images via Deep Learning Based Object Detection: A Case Study on an Extremely Small Data Set

ObjectivesLaryngoscopy is a medical procedure for obtaining a view of the human larynx. It is challenging for clinicians to distinguish laryngeal neoplasms by human visual observation. Recent deep learning methods can assist clinicians in improving the accuracy of distinguishing. However, existed methods are often trained on large-scale private datasets, while other researchers and hospitals can neither access these private datasets nor afford to build such large-scale datasets. In this paper, we focus on identifying laryngeal neoplasms under the “small data” regime, which is more important for many small hospitals to investigate deep learning models for diagnosis. Material and methodsWe build an extremely small dataset consisting of 279 laryngoscopic images of different categories. We found that traditional deep learning models for image classification cannot achieve satisfactory performance for small data, due to the great variability of recording laryngoscopic images and the small area of the neoplasms. To address these difficulties, we propose to employ object detection methods for this small data problem. Concretely, a Faster R-CNN is implemented here, which combines the DropBlock regularization technique to alleviate overfitting additionally. ResultsCompared to previous methods, our model is more robust to overfitting and can predict the location and category of detected neoplasms simultaneously. Our method achieves 73.00% overall accuracy, which is higher than the average of clinicians (65.05%) and the recent state-of-the-art classification method (65.00%). ConclusionThe proposed method shows great ability to detect both the category and location of neoplasms and can be served as a screening tool to help the final decisions of clinicians.

Multiclass Anomaly Detection of Bridge Monitoring Data with Data Migration between Different Bridges for Balancing Data

Structural health inspection systems are widely used to manage and maintain infrastructure that involves massive sensor devices. However, these sensors receive the natural environment or external factors in the long-term exposure to the outdoor environment, resulting in the failure of the sensors, which causes multiple categories of abnormal data in the collected data. The data often is unbalanced due to the random nature of failures. This unbalanced anomaly data poses a major challenge to existing anomaly detection methods and will affect the effectiveness of the information provided by the structural health monitoring system. In the paper, a data migration method is proposed to migrate bridge data to the target bridge dataset for expansion so that the number of images of different categories in the target bridge dataset increases. This method can be divided into three steps: firstly, to classify the data; secondly, to determine the suitability of the data and to construct the dataset; and finally, to train the data. The comparative validation is used to compare the training performance of the dataset using data migration with the dataset only using the target bridge to analyze the abnormal data identification in each category. In the experiment, the recall of some categories of data reached a significant increase of more than 30%, achieving better identification of various categories of abnormal data. Adopting the method of data migration between different bridges can solve the impact of imbalanced data and improve the recognition performance of categories with fewer images.

EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN

In this work, a multi-task convolutional neural network with multi-input (MIMT-CNN) is proposed for electromagnetic interference (EMI) signals recognition and electromagnetic environment risk evaluation of the data link of unmanned aerial vehicle (UAV). The visualized performance parameters, short-time Fourier transform (STFT) spectrograms, and constellation diagrams are obtained by experiment on the electromagnetic susceptibility of UAV’s datalink. In particular, the constellation diagram is further enhanced by calculating the density distribution of sampling points to obtain the normalized density constellation. Taking the above different categories of images as the input of the expected model, the multi-element and high correlation EMI features are extracted and fused in the MIMT-CNN. Besides, the structure of series-parallel connection is adopted in the trained model and the Bayesian optimization is also used to select hyperparameters. In this case, the perception model with higher reliability can be obtained. On this basis, the performance and complexity of the obtained model with different input channels are compared. The results show that with the input of constellation diagram, especially the normalized density constellation, can significantly improve the accuracy of the model. Besides the normalized density constellation, the model with visualized performance parameters and STFT spectrogram as inputs has a much better performance.

Application of Recurrent Neural Network Algorithm in Intelligent Detection of Clinical Ultrasound Images of Human Lungs.

Lung ultrasound has great application value in the differential diagnosis of pulmonary exudative lesions. It has good sensitivity and specificity for the diagnosis of various pulmonary diseases in neonates and children. It is believed that it can replace chest CT examination. It is routinely used for the diagnosis of pulmonary diseases in emergency critical care medicine. However, the interpretation of the impact of ultrasound on the human lungs relies heavily on experienced physicians, which greatly restricts the interpretation efficiency of the impact of ultrasound. In order to improve the efficiency of monitoring and interpretation of the impact of ultrasound, this paper proposes an intelligent detection algorithm for human lung clinical ultrasound images based on recurrent neural network. Transfer learning is used to replace the fully connected layer of the VGG16 model and improve the loss function, so that the same the Euclidean distance between category images can be reduced, and the Euclidean distance between different categories of images can be increased, enhancing the resolution of the entire model, thereby achieving better image feature extraction results. The experimental results show that the algorithm proposed in this paper can surpass the doctor's level in the identification of various lung diseases.

Behavioral and Brain Reactivity Associated With Drug-Related and Non-Drug-Related Emotional Stimuli in Methamphetamine Addicts.

BackgroundMethamphetamine addicts can experience severe emotional processing disorders, with abnormal responses to emotional and drug-related stimuli. These aberrant behaviors are one of the key factors leading to relapse. Nevertheless, the characteristics of addicts’ responses to drug-related stimuli and their responses to emotional stimuli remain controversial.Methods52 methamphetamine addicts from China passively viewed three different categories of images: Drug-related; positive emotional; and negative emotional. In the first task, participants completed a 9-point Self-Assessment Manikin (SAM) scale, rating the valence of each image. In the second, they performed a cued-action task while electroencephalography (EEG) data were recorded.ResultDrug-related images were rated negatively, with an average rating of 3.57. However, reaction times to drug-related stimuli were significantly faster than for negative stimuli (p = 0.030), and were indistinguishable from positive stimuli (p > 0.99). Similarly, EPN amplitudes evoked by drug-related images were significantly larger than those evoked by negative stimuli (p < 0.001), but no different than positive stimuli (p > 0.99). LPP amplitudes evoked by drug-related stimuli were significantly smaller than those evoked by negative (p < 0.001) and positive stimuli (p = 0.004).ConclusionDespite negative self-assessments of drug-related imagery, MA-addicts reaction times were no slower than positive reactions. Similarly, drug-related and positive imagery EPN amplitudes were indistinguishable. Together, these results suggest increased attentional resources were allocated to the processing of drug-related stimuli and the pathways responsible partially overlap with the those recruited in processing positive emotional imagery in addicts. Moreover, in the late stage of visual processing, MA-addicts showed reduced brain activity in response to drug-related stimuli, suggesting reverse inhibition in response preparation and emotional appraisal. These findings may provide a reference for clinicians treating drug-taking behavior and for the development of new models of rehabilitation therapy.

Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc

Many studies use different categories of images to define their conditions. Since any difference between these categories is a valid candidate to explain category-related behavioral differences, knowledge about the objective image differences between categories is crucial for the interpretation of the behaviors. However, natural images vary in many image features and not every feature is equally important in describing the differences between the categories. Here, we provide a methodological approach to find as many of the image features as possible, using machine learning performance as a tool, that have predictive value over the category the images belong to. In other words, we describe a means to find the features of a group of images by which the categories can be objectively and quantitatively defined. Note that we are not aiming to provide a means for the best possible decoding performance; instead, our aim is to uncover prototypical characteristics of the categories. To facilitate the use of this method, we offer an open-source, MATLAB-based toolbox that performs such an analysis and aids the user in visualizing the features of relevance. We first applied the toolbox to a mock data set with a ground truth to show the sensitivity of the approach. Next, we applied the toolbox to a set of natural images as a more practical example.

System for neural network recognition of malignant pigmented skin neoplasms with image pre-processing

The article presents a system for the recognition of malignant pigmented skin neoplasms with a preliminary processing stage. Image pre-processing consists of removing hair structures from images, as well as resizing images and their further augmentation. Augmentation made it possible to increase the variety of training data, balance the number of images in different categories, and avoid retraining the neural network. The modeling was carried out using the MatLab R2020b software package for solving technical calculations on clinical dermatoscopic images from the international open archive ISIC Melanoma Project. The proposed system for the recognition of malignant pigmented skin neoplasms made it possible to increase the accuracy of image classification up to 80.55%. The use of the proposed recognition system will make it possible to increase the efficiency and quality of diagnosis, in comparison with the methods of visual diagnosis.

Classification and Diagnosis of Residual Thyroid Tissue in SPECT Images Based on Fine-Tuning Deep Convolutional Neural Network.

Patients with thyroid cancer will take a small dose of 131I after undergoing a total thyroidectomy. Single-photon emission computed tomography (SPECT) is used to diagnose whether thyroid tissue remains in the body. However, it is difficult for human eyes to observe the specificity of SPECT images in different categories, and it is difficult for doctors to accurately diagnose the residual thyroid tissue in patients based on SPECT images. At present, the research on the classification of thyroid tissue residues after thyroidectomy is still in a blank state. This paper proposes a ResNet-18 fine-tuning method based on the convolutional neural network model. First, preprocess the SPECT images to improve the image quality and remove background interference. Secondly, use the preprocessed image samples to fine-tune the pretrained ResNet-18 model to obtain better features and finally use the Softmax classifier to diagnose the residual thyroid tissue. The method has been tested on SPECT images of 446 patients collected by local hospital and compared with the widely used lightweight network SqueezeNet model and ShuffleNetV2 model. Due to the small data set, this paper conducted 10 random grouping experiments. Each experiment divided the data set into training set and test set at a ratio of 3:1. The accuracy and sensitivity rates of the model proposed in this paper are 96.69% and 94.75%, which are significantly higher than other models (p < 0.05). The specificity and precision rates are 99.6% and 99.96%, respectively, and there is no significant difference compared with other models. (p > 0.05). The area under the curve of the proposed model, SqueezeNet, and ShuffleNetv2 are 0.988 (95% CI, 0.941–1.000), 0.898 (95% CI, 0.819–0.951) (p = 0.0257), and 0.885 (95% CI, 0.803–0.941) (p = 0.0057) (p < 0.05). We prove that this thyroid tissue residue classification system can be used as a computer-aided diagnosis method to effectively improve the diagnostic accuracy of thyroid tissue residues. While more accurately diagnosing patients with residual thyroid tissue in the body, we try our best to avoid the occurrence of overtreatment, which reflects its potential clinical application value.

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity.

Salient object detection represents a novel preprocessing stage of many practical image applications in the discipline of computer vision. Saliency detection is generally a complex process to copycat the human vision system in the processing of color images. It is a convoluted process because of the existence of countless properties inherent in color images that can hamper performance. Due to diversified color image properties, a method that is appropriate for one category of images may not necessarily be suitable for others. The selection of image abstraction is a decisive preprocessing step in saliency computation and region-based image abstraction has become popular because of its computational efficiency and robustness. However, the performances of the existing region-based salient object detection methods are extremely hooked on the selection of an optimal region granularity. The incorrect selection of region granularity is potentially prone to under- or over-segmentation of color images, which can lead to a non-uniform highlighting of salient objects. In this study, the method of color histogram clustering was utilized to automatically determine suitable homogenous regions in an image. Region saliency score was computed as a function of color contrast, contrast ratio, spatial feature, and center prior. Morphological operations were ultimately performed to eliminate the undesirable artifacts that may be present at the saliency detection stage. Thus, we have introduced a novel, simple, robust, and computationally efficient color histogram clustering method that agglutinates color contrast, contrast ratio, spatial feature, and center prior for detecting salient objects in color images. Experimental validation with different categories of images selected from eight benchmarked corpora has indicated that the proposed method outperforms 30 bottom-up non-deep learning and seven top-down deep learning salient object detection methods based on the standard performance metrics.

Near Duplicate Image Detection Using Image Net Model

The rapid development in the technology of Internet and the increase in the usage of mobile devices, it is very easy for users to capture, communicate and share the images through the networks. The spectacular achievement of convolution neural networks in the area of computer vision, will help us to match the features that are very similar between the images for detecting the duplicate version of the image. In this project we use Image Net model that mainly provide a large database that contains many images of different categories. Flask framework is used in this project, which includes many libraries, modules that helps the web developer to write web application. In this project the user is allowed to upload the image, then the image features will be extracted and fed to the CNN model. The CNN model will calculate the similarity distance between the images that is already present in the database and detect the top four images that are duplicate version of the uploaded image.

Classification of rock fragments produced by tunnel boring machine using convolutional neural networks

Rock fragmentation in tunnel boring machine (TBM) construction is an important indicator of tunnelling state. Large fragments often correspond to an unhealthy tunnelling state and may cause physical damage to belt conveyors. In this work, a convolutional neural network (CNN), AlexNet, was modified to perform image classification based on the volume of the largest fragment. First, all images were captured at the tunnel construction site. Second, a classification scheme was proposed according to the geological state of the site. After pre-processing raw images, different categories of images were labelled manually to train and evaluate CNN models. Finally, an optimised parameter setting for rock fragment recognition was carried out. Experimental results showed that the extracted features are reliable and can distinguish different types of images well. This competitive performance demonstrates that the proposed methodology can accurately detect large rock fragments by images at the site.

Regularized deep clustering approach for effective categorization of maize diseases

The disease prevalence among different plant species reduces the grain production, significantly affects the growth in terms of quantity and quality. Early diagnosis of the ailment effectively minimizes the plant devastation and improves the condition. Mostly, people involved in the agriculture-related activity identifies the abnormalities in the plants. However, in some cases, the chance of manual error is high during the diagnosis of the plant status. It further leads to economic deprivation, affects the livelihood of the agriculturalists. Effective monitoring and diagnosing plant conditions can be automated with available tools and applications. Such systems serve as virtual assistance to the needy anytime, which fills the gap of the presence of domain expertise. Moreover, automated screening tools contribute to elevated agricultural activities and ensures security upon food production and availability. These applications are powered by advanced, intelligent computational models for an accurate diagnosis. Deep learning algorithms are gaining more attention in the field of real-time monitoring and assistance due to its higher precision over complex applications. However, not all the models fit for applications with different environmental conditions. In this paper, a deep cluster-based plant disease categorization system is proposed to find the discriminative patterns from the images of different categories of maize crop. The proposed regularized deep clustering (RDC) algorithm combines the effectiveness of the convolutional autoencoder model alongside local structure preservation constraints and regularization. The proposed model is compared with state-of-the-art deep clustering algorithms to exhibit the efficacy of the RDC under different modes of clustering performance evaluation. This system acts as an effective tool for targeted users by providing them better assistance on plant disease diagnosis.

FEM: Feature extraction and mapping for radio modulation classification

Due to the stochastic nature of wireless channels, the received radio signal is noised during transmission causing difficulty in classifying radio modulation categories. Deep learning methods are applied to radio modulation classification for its promising performance compared with traditional approaches. However, deep learning models have relied on a large number of labeled data as training data. Collecting and labeling radio signals in various environments require a lot of manpower and material resources, which will limit the application of deep learning models in modulation recognition. To improve the efficiency of labeling radio signals, in this paper, we propose a feature extraction and mapping algorithm (FEM) to extract and map radio signals to images for further classification. Specifically, we propose an LSTM-based feature extraction network to extract radio features from radio signals, which are further mapped to different categories of images via a feature mapping network. The mapping between radio features and images is automatically learned by a generative adversarial network. Experiment results show that different modulation categories can be obviously discriminated though the mapped images. Furthermore, the modulation classification accuracy based on mapped images is slighted improved comparing with the original radio signals.

Batch Similarity Based Triplet Loss Assembled into Light-Weighted Convolutional Neural Networks for Medical Image Classification.

In many medical image classification tasks, there is insufficient image data for deep convolutional neural networks (CNNs) to overcome the over-fitting problem. The light-weighted CNNs are easy to train but they usually have relatively poor classification performance. To improve the classification ability of light-weighted CNN models, we have proposed a novel batch similarity-based triplet loss to guide the CNNs to learn the weights. The proposed loss utilizes the similarity among multiple samples in the input batches to evaluate the distribution of training data. Reducing the proposed loss can increase the similarity among images of the same category and reduce the similarity among images of different categories. Besides this, it can be easily assembled into regular CNNs. To appreciate the performance of the proposed loss, some experiments have been done on chest X-ray images and skin rash images to compare it with several losses based on such popular light-weighted CNN models as EfficientNet, MobileNet, ShuffleNet and PeleeNet. The results demonstrate the applicability and effectiveness of our method in terms of classification accuracy, sensitivity and specificity.

Enhanced segmentation network with deep learning for Biomedical waste classification

Objective: To maximize the accuracy of classifying the medical wastage, an Enhanced Segmentation Network (EnSegNet) with Deep Neural Network-Trash Classification (EnSegNet-DNN-TC) is proposed in this article. Methods: Initially, a core trainable segmentation network called SegNet framework is proposed which uses the Encoder-Decoder Network (EDN) and a pixel-wise classification layer for image segmentation. The decoder is used to upsample its low-resolution input feature maps via max-pooling. Also, SegNet uses fewer parameters for training. The uncertainty inherent to the EDN is modeled by the Bayesian functions to segment the input images. But, this SegNet can sample a limited amount of pixels in the images. Hence, an EnSegNet is proposed that uses Content-Sensitive Sampling (CSS) to sample more pixels in the data-sparse regions and fewer pixels in data-dense regions. Once the segmentation is completed, the DNN is applied for classifying the wastage using the segmented images. Findings: The experimental results show that the EnSegNet-DNN-TC framework achieves 88% accuracy compared to the DNN-TC for considering 100 images of different categories of biomedical wastes from the trash image dataset. Keywords: Biomedical wastage classification; deep learning; image segmentation; ResNext; encoder-decoder network

Generating Sea Surface Object Image Using Image-to-Image Translation

Abstract Sea objects training, the conditional adversarial networks require a large number of images to solve image-to-image translation problems. In the case of insufficient samples, it leads to network overfitting and poor training results. This project proposes a conditional adversarial generative model that retains the original background features in the absence of paired samples. The goal of this project is to reduce the deviation of the corresponding output from the original input. Firstly, the object images of different categories are labeled with color masks. Second, sea objects are generated randomly in the original background using model of this project. Finally, the generated results of this approach are compared with other approaches. The experimental results show that, compared with results from other conditional adversarial generative models, the generated object images using model of this project have the characteristics of richer texture and clearer structure.