Image Aesthetics Assessment Using Multi Channel Convolutional Neural Networks

The influencing factors of community risk are complex. For the low accuracy of traditional prediction model, a multichannel convolutional neural network community risk prediction model is proposed by improving convolutional neural network of deep learning. First of all, in the community risk prediction model, the structure of multichannel input convolutional neural network is selected. Then, add it into the full connection layer. Subsequently, the DenseNet layer is added to establish connections between different network layers. Finally, the receptive field is improved, and the gradient disappearance is solved. Thus, the prediction accuracy of model is improved. Compared with the traditional model, the proposed multichannel convolutional neural network model has better prediction accuracy. In addition, it performs better on the three indicators, namely, correlation coefficient R , coefficient of determination R 2 , and mean square root error RMSE. Compared with the commonly used LSTM model and logic regression model, the proposed model also has certain advantages, which is more suitable for community risk prediction.

Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals

Image Aesthetics Assessment (IAA) is a challenging task due to its subjective nature and expensive manual annotations. Recent large-scale vision-language models, such as Contrastive Language-Image Pre-training (CLIP), have shown their promising representation capability for various downstream tasks. However, the application of CLIP to resource-constrained and low-data IAA tasks remains limited. While few attempts to leverage CLIP in IAA have mainly focused on carefully designed prompts, we extend beyond this by allowing models from different domains and with different model sizes to acquire knowledge from CLIP. To achieve this, we propose a unified and flexible two-phase CLIP-based Semi-supervised Knowledge Distillation (CSKD) paradigm, aiming to learn a lightweight IAA model while leveraging CLIP’s strong generalization capability. Specifically, CSKD employs a feature alignment strategy to facilitate the distillation of heterogeneous CLIP teacher and IAA student models, effectively transferring valuable features from pre-trained visual representations to two lightweight IAA models, respectively. To efficiently adapt to downstream IAA tasks in a low-data regime, the two strong visual aesthetics learners then conduct distillation with unlabeled examples for refining and transferring the task-specific knowledge collaboratively. Extensive experiments demonstrate that the proposed CSKD achieves state-of-the-art performance on multiple widely used IAA benchmarks. Furthermore, analysis of attention distance and entropy before and after feature alignment shows the effective transfer of CLIP’s feature representation to IAA models, which not only provides valuable guidance for the model initialization of IAA but also enhances the aesthetic feature representation of IAA models. Code will be made publicly available.

Low-quality finger vein images seriously affect the performance of the recognition system. Aiming at filtering high- and low-quality finger vein images, this paper presents a finger-vein image quality assessment method based on light-CNN (convolutional neural network). The previous quality assessment method aims to establish a model that is consistent with the evaluation effect of the human visual system. In contrast, our model further evaluates the quality of the finger vein image for the purpose of distinguishing whether the image contains rich and stable vein characteristics. The rich and stable vein characteristics in finger vein images play a more important role in practical applications. First, the finger vein image is automatically annotated based on a traditional finger vein image quality assessment method; secondly, the finger vein image is cut into image blocks to expand the training set, and the image block instead of the entire image is input to the CNN; then, the network and its variants are trained; in the final test, the average quality score of multiple image blocks from an image is the final quality score of the image. The experimental results show that the proposed light-CNN accurately identifies high and low quality images and significantly outperforms existing approaches in terms of distinguishing whether there are rich and stable vein characteristics in the image.

The excellent feature extraction ability of deep convolutional neural networks (DCNNs) has been demonstrated in many image processing tasks, by which image classification can achieve high accuracy with only raw input images. However, the specific image features that influence the classification results are not readily determinable and what lies behind the predictions is unclear. This study proposes a method combining the Sobel and Canny operators and an Inception module for ship classification. The Sobel and Canny operators obtain enhanced edge features from the input images. A convolutional layer is replaced with the Inception module, which can automatically select the proper convolution kernel for ship objects in different image regions. The principle is that the high-level features abstracted by the DCNN, and the features obtained by multi-convolution concatenation of the Inception module must ultimately derive from the edge information of the preprocessing input images. This indicates that the classification results are based on the input edge features, which indirectly interpret the classification results to some extent. Experimental results show that the combination of the edge features and the Inception module improves DCNN ship classification performance. The original model with the raw dataset has an average accuracy of 88.72%, while when using enhanced edge features as input, it achieves the best performance of 90.54% among all models. The model that replaces the fifth convolutional layer with the Inception module has the best performance of 89.50%. It performs close to VGG-16 on the raw dataset and is significantly better than other deep neural networks. The results validate the functionality and feasibility of the idea posited.

Automated image classification is an essential task of the computer vision field. The tagging of images into a set of predefined groups is referred to as image classification. The implementation of computer vision to automate image classification would be beneficial because manual image evaluation and identification can be time-consuming, particularly when there are many images of different classes. Deep learning approaches are proven to overperform existing machine learning techniques in many fields in recent years, and computer vision is one of the most notable examples. The very deep neural network (VDCNN) is a powerful deep learning model for image classification, and this paper examines it briefly using MNIST handwritten digit dataset. This dataset is used to prove the efficacy of very deep neural networks over other deep learning models. The proposed study aims to comprehend the very deep neural network architecture used to accomplish a handwritten digit recognition task. The feasibility of the proposed model is evaluated using mean accuracy, validation accuracy, and standard deviation. The study results of the very deep neural network model are compared to a convolutional neural network and convolutional neural network with batch normalization. According to the results of the comparison study, very deep neural networks achieve high accuracy of 99.1% for a handwritten dataset. The outcome of the proposed work is used to interpret how well a very deep neural network performs when compared to the other two models of deep neural networks. This proposed architecture may be used to automate the classification of handwritten digits dataset.

Deep convolutional neural networks have become a dominant solution for numerous image classification tasks. However, a main criticism is the poor explainability due to the black-box characteristic, which hurdles the extensive usage of deep convolutional neural networks. To address this issue, this paper proposes a new evolutionary multi-objective based method, which aims to explain the behaviours of deep convolutional neural networks by evolving local explanations on specific images. To the best of our knowledge, this is the first evolutionary multi-objective method to evolve local explanations. The proposed method is model-agnostic, i.e. it is applicable to explain any deep convolutional neural networks. ImageNet is used to examine the effectiveness of the proposed method. Three well-known deep convolutional neural networks -VGGNet, ResNet, and MobileNet, are chosen to demonstrate the modelagnostic characteristic. Based on the experimental results, it can be observed that the local explanations are understandable to end-users, who need to check the sensibility of the evolved explanations to decide whether to trust the predictions made by the deep convolutional neural networks. Furthermore, the local explanations evolved by the proposed method improves the confidence of deep convolutional neural networks making the predictions. Lastly, the pareto front and convergence analyses indicate that the proposed method can form a good set of nondominated solutions.

In recent years Neural Networks and especially Deep Neural Networks (DNN) have seen a rise in popularity. DNNs have increased the overall performance of algorithms in applications such as image recognition and classification, 2D and 3D pose detection, natural language processing, and time series forecasting. Especially in image classification and recognition, so-called Convolutional Neural Network (CNN) have gained high interest as they can reach a high accuracy, which makes them a viable solution in cancer detection or autonomous driving. As CNNs are widely used in image tasks, different visualizations techniques have bee developed to show how their internals are working. Apart from image tasks, CNNs are applicable to other problems, e.g., time series classification, time series forecasting, or natural language processing. CNNs in those contexts behave similarly, allowing for the same visualization techniques to make them more interpretable. In this chapter, we adapt image visualization algorithms to time series problems, allowing us to build granular, intuitively interpretable feature hierarchies to make a time series forecast as understandable as an image recognition task. We do so by using our previous work on power time series forecasting using CNN Auto Encoders (AEs) and applying typical CNN visualization techniques to it. Thus, we guid computer scientists to provide better interpretable figures for a time series forecasting task to application domain experts.KeywordsVisualizationConvolutional neural networksTime series data

MRAM: Multi-scale Regional Attribute-weighting via Meta-learning for Personalized Image Aesthetics Assessment

Color temperature, as a crucial attribute influencing image color, plays a critical role in Image Aesthetics Assessment (IAA). Yet, within the existing IAA field, little light has been shed on assessing the aesthetic quality of image color temperature. To bridge this gap, we introduce a new task: Image Color Temperature Aesthetics Assessment (ICTAA). However, this task poses the following challenges: 1) Perceptual Sensitivity: humans exhibit high sensitivity to subtle shifts in color temperature, necessitating a model to enable fine-grained discrimination; 2) Spectral Continuity: The theoretical modeling of color temperature aesthetics requires continuous labels; however, the just-noticeable-difference property of human perception makes continuous labeling infeasible, necessitating a well-designed labeling strategy. To address the aforementioned challenges, we make the following efforts. First, we propose a multi-modal contrastive learning framework, ICTA2Net, that models color temperature differences between image pairs while strictly controlling other visual attributes. Second, leveraging color temperature transitivity, we design a weakly supervised strategy that discretely samples images based on anchor images and human perception to build contrastive relations across color temperatures, enabling learning from discrete labels. Thirdly, we construct a color temperature aesthetics dataset, ICTAA240K, and a benchmark for validation. Additionally, we propose a new metric, Information Entropy-weighted Accuracy (IEA), which weights accuracy by the degree of annotation disagreement to reflect model performance across varying sample difficulties, complementing existing evaluation metrics. Experiments show our method outperforms existing state-of-the-art IAA methods on ICTAA240K, thereby setting an effective roadmap for ICTAA.

In this work, we point out that the major dilemma of image aesthetics assessment (IAA) comes from the abstract nature of aesthetic labels. That is, a vast variety of distinct contents can correspond to the same aesthetic label. On the one hand, during inference, the IAA model is required to relate various distinct contents to the same aesthetic label. On the other hand, when training, it would be hard for the IAA model to learn to distinguish different contents merely with the supervision from aesthetic labels, since aesthetic labels are not directly related to any specific content. To deal with this dilemma, we propose to distill knowledge on semantic patterns for a vast variety of image contents from multiple pre-trained object classification (POC) models to an IAA model. Expecting the combination of multiple POC models can provide sufficient knowledge on various image contents, the IAA model can easier learn to relate various distinct contents to a limited number of aesthetic labels. By supervising an end-to-end single-backbone IAA model with the distilled knowledge, the performance of the IAA model is significantly improved by 4.8% in SRCC compared to the version trained only with ground-truth aesthetic labels. On specific categories of images, the SRCC improvement brought by the proposed method can achieve up to 7.2%. Peer comparison also shows that our method outperforms 10 previous IAA methods.

Brain tumor implies development of strange cells in brain. In cutting edge stages, brain tumor is most risky infection which can't be relieved. Thus, it ought to be identified in the beginning phases with the assistance of MRI (Magnetic Resonance Image). So, there will be more changes to the patient to endure. Quite possibly, the most functional and significant technique is to utilize Deep Neural Network (DNN). In this paper, a Deep Convolutional Neural Network (DCNN) based on VGG16 model has been developed to identify a tumor through brain Magnetic Resonance Imaging (MRI) dataset. In the clinical field, the strategies of machine learning (ML) and data mining hold a critical stand. It is effectively used to achieve the efficiency and exact location of tumor. The proposed technique involves automatic segmentation method based on Deep Convolution neural network (DCNN). It is layer based segmentation and classification technique. Different levels are engaged with the proposed technique, first step is data collection and then pre-processing & average filtering is done, after that segmentation, feature extraction and classification via DCNN is performed. In this paper, a fresh technique for classification of brain MRI images has been proposed using DCNN based softmax Classifier. The proposed system has been compared with the existing ones. The results are really encouraging as the loss is reduced and accuracy is increased. The proposed system achieved the training accuracy of 98.9% and validation accuracy of 100%. The training loss is reduced up to 0.0230 and the validation loss reduced up to 0.0109.

Using Convolutional Neural Networks for Enhanced Capture of Breast Parenchymal Complexity Patterns Associated with Breast Cancer Risk

BackgroundTo study the impact of image quality on quantitative measurements and the frequency of segmentation error with optical coherence tomography angiography (OCTA).MethodsSeventeen eyes of 10 healthy individuals were included in this study. OCTA was performed using a swept-source device (Triton, Topcon). Each subject underwent three scanning sessions 1–2 min apart; the first two scans were obtained under standard conditions and for the third session, the image quality index was reduced using application of a topical ointment. En face OCTA images of the retinal vasculature were generated using the default segmentation for the superficial and deep retinal layer (SRL, DRL). Intraclass correlation coefficient (ICC) was used as a measure for repeatability. The frequency of segmentation error, motion artifact, banding artifact and projection artifact was also compared among the three sessions.ResultsThe frequency of segmentation error, and motion artifact was statistically similar between high and low image quality sessions (P = 0.707, and P = 1 respectively). However, the frequency of projection and banding artifact was higher with a lower image quality. The vessel density in the SRL was highly repeatable in the high image quality sessions (ICC = 0.8), however, the repeatability was low, comparing the high and low image quality measurements (ICC = 0.3). In the DRL, the repeatability of the vessel density measurements was fair in the high quality sessions (ICC = 0.6 and ICC = 0.5, with and without automatic artifact removal, respectively) and poor comparing high and low image quality sessions (ICC = 0.3 and ICC = 0.06, with and without automatic artifact removal, respectively).ConclusionsThe frequency of artifacts is higher and the repeatability of the measurements is lower with lower image quality. The impact of image quality index should be always considered in OCTA based quantitative measurements.

The report presents an algorithm for constructing a convolutional hierarchical neural network classifier, which is a modification of the algorithm for constructing hierarchical neural network classifiers suggested before. The original algorithm was designed to exploit intrinsic class hierarchy to build a class tree with a neural network in each node classifying groups of initial classes (in a non-terminal node) or a subset of original classes (in a terminal node). The convolutional modification utilizes convolutional neural networks instead of regular fully connected networks in order to apply the model to image classification tasks. Use of class hierarchy for image classification should reduce the number of adjusted neural network parameters compared to deep convolutional neural networks, and therefore it should reduce training and inference time. In this context the algorithm may be compared with some pruning techniques. The convolutional hierarchical neural network classifier inherits some hyperparameters of a conventional hierarchical neural network classifier, like the activation threshold and the threshold by the share of voting patterns. The goal of this study was to explore different strategies of choosing these hyperparameters. To test these strategies, we used the CIFAR-10 dataset. Also, for demonstration purposes we apply the convolutional hierarchical neural network classifier to the CIFAR-100 dataset.