A qubit as a Kernel: an efficient quantum-classical network for image classification with Quantum Independent Convolution-like Kernel Layer

Image classification is typically performed with highly trained feed-forward machine learning algorithms like deep neural networks and support vector machines. The image can be treated as a time-series input when applied to the network multiple times, opening the way for recurrent neural networks to perform tasks like image classification, semantic segmentation and auto-encoding. With this approach, ultra-fast training, network optimization, and short-term memory effects allows for dynamic, low-volume datasets to be quickly learned without heavy image pre-processing or feature extraction; the main limitation being that input images need labeled output images for training, as is also true of most standard approaches. In this work, the MNIST handwritten digit dataset is used as a benchmark to evaluate metrics of a modified Echo State Network for static image classification. The image array is passed through a noise filter multiple times as the Echo State Network converges to a classification. This highly dynamic approach easily adapts to sequential image (video) tasks like object tracking and is effective with small datasets. Classification rates reach 95.3% with sample size of 10000 handwritten digits and training time of approximately 5 minutes. Progression of this research enables discrete image and time-series classification under a single algorithm, with low computing power and memory requirements.

Within this study, we made a comparison of the efficiency of several convolutional neural networks with different architectures taken from the Tensorflow open-source library. The paper analyzes the parameters of these networks using validation curves. The InceptionV3 model showed the best results for the X-ray image binary classification task. The selected convolutional network with optimal parameters obtained in this study is easy to be retrained using its own data set and applied to solve classification tasks, select and segment objects on X-ray images.

CMNet: Classification-oriented multi-task network for hyperspectral pansharpening

In recent years, deep learning models, which possess powerful feature extraction abilities, have achieved remarkable success in the classification of hyperspectral images (HSIs). Nevertheless, a common challenge faced by most deep learning models, including few-shot learning models, is the scarcity of valid labeled samples. To address this issue, we propose a cross-domain self-taught network (CDSTN) for few-shot hyperspectral image classification. The proposed CDSTN merges domain adaptation and semi-supervised self-taught strategy to implement the few-shot learning, which utilizes adequate labeled and unlabeled samples from source as well as target domain respectively. For the feature information extraction of HSI, we propose a deep spatial-spectral feature embedded extractor composed of four residual blocks and a channel attention module. Additionally, a set of domain classifiers are introduced behind each residual block for the purpose of domain alignment by extracting more domain information at different depths of the network. Finally, plenty of unlabeled samples are assigned with pseudo labels through the trained network, and a pseudo label refinement module is designed to select the most confident pseudo label sample for each class to further enrich the labeled database of target domain. Experiments conducted on four widely used benchmark HSI data sets demonstrate that CDSTN can obtain superior and stable performance with limited labeled samples compared with some state of the arts.

ABSTRACTConvolutional neural network (CNN) for hyperspectral image classification can provide excellent performance when the number of labeled samples for training is sufficiently large. Unfortunately, a small number of labeled samples are available for training in hyperspectral images. In this letter, a novel semi-supervised convolutional neural network is proposed for the classification of hyperspectral image. The proposed network can automatically learn features from complex hyperspectral image data structures. Furthermore, skip connection parameters are added between the encoder layer and decoder layer in order to make the network suitable for semi-supervised learning. Semi-supervised method is adopted to solve the problem of limited labeled samples. Finally, the network is trained to simultaneously minimize the sum of supervised and unsupervised cost functions. The proposed network is conducted on a widely used hyperspectral image data. The experimental results demonstrate that the proposed approach provides competitive results to state-of-the-art methods.

Researchers encounter substantial challenges in medical image classification, mainly due to limited image resolution and low signal-to-noise ratios. This situation makes it difficult for deep learning algorithms to identify abnormal regions based solely on image content accurately. This paper proposes ResGDANet (Residual Group Dual-Channel Attention Network), an enhanced architecture that builds upon ResGANet by incorporating two novel modules: a Dual-Channel Attention Fusion (DCAF) module and a Retention-Memory Transformer (RMT) module. The DCAF module utilizes a dual-path architecture that integrates global average pooling and max pooling operations, effectively enhancing local feature representation through the fusion of channel-wise and spatial attention mechanisms. The RMT module enhances rotation-invariant feature extraction by integrating the retention mechanism from Retentive Networks and the global context modeling capabilities of Vision Transformers. Extensive experiments on the COVID19-CT and ISIC2018 datasets demonstrate the superiority of ResGDANet, achieving classification accuracies of 83.74% and 81.73% respectively, outperforming state-of-the-art models including ResGANet, GvT, and SENet. Ablation studies and visualization analyses further validate the efficacy of the proposed attention module, showing notable enhancements in feature representation capability and classification accuracy. By introducing a more robust and precise classification framework, this research contributes importantly to the progress in medical image analysis.

MCFD-Former: An efficient lightweight feature-extraction network for hyperspectral image classification with limited samples

Objectives: The paper presents application of convolution neural network and artificial neural network for image classification problem for clothing dataset along with their performance comparison against different optimizers. The major objective of this paper is to perform image classification on fashion-mnist clothing dataset images. Methods: The methods used here are, the traditional ANN and CNN. Here image classification is performed on Fashion-mnist, clothing dataset using CNN and ANN with different optimizers. The performance of the working of ANN and CNN in classifying images from fashion-mnist dataset is compared against different optimizers namely stochastic gradient Descent, Adagrad, RMS prop and Adam optimizer. Findings: The study found that CNN worked better than ANN yielding training accuracy of 95%, 93% and testing accuracy of 91%, 89% when used with Adam and RmsProp respectively. Novelty: The novelty of this work is to present a comparative study of image classification using CNN, ANN using different optimizers, since not many studies or research articles showed the performance comparison of traditional and convolution neural networks in image classification along with different optimizers. Since the real-world scenarios of today require enormous data to be processed, CNN can fit well to diversify applications since they highly reduce the number of parameters to be trained that speeds up the training process. Moreover, to be specific on image classification problems they require the best and most prominent features to be detected and uncovered; this can be achieved using CNN since it has the concept of convolution using filters at its Core. Hence, CNN is highly recommended for such image classification applications than the traditional artificial-neural-networks because of the aforementioned reasons. Keywords: ANN (Artificial NeuralNetworks); CNN (ConvolutionNeural Networks); Optimization; SGD (Stochastic Gradient Descent); RmsProp (Root mean Square propagation); Adam (Adaptive moment estimation); AdaGrad (Adaptive Gradient)

Image Classification is one of the important processing methods in the field of image processing. Traditional Classification algorithms, such as KNN (K-NearestNeighbor), have been unable to meet the accuracy standards of image Classification. And convolutional neural networks (CNN) are increasingly used to solve the problem of image classification. This paper briefly ces the convolutional neural network, which is constructed using the TensorFlow deep learning framework, and conducts experiments on 2000 pieces of custom data set of 10 classification obtained by crawler technology. At the same time, a comparative experiment is conducted on the traditional image classification KNN algorithm. Experimental results show that the application of convintroduolutional neural network in image classification has relatively large advantages, and the accuracy has been greatly improved.

With the development of deep learning, CNNs have become mainstream in image classification. However, CNN performance is affected by factors like loss function choice and optimization algorithm. This paper aims to improve CNN performance in image classification by optimizing SGD. Firstly, we analyze different loss functions and propose a suitable optimization strategy. Second, we explore SGD and its variants' roles in CNN training, focusing on accelerating convergence and improving accuracy. Experiments show the proposed scheme effectively enhances CNN performance, providing a reliable solution for image classification. This study supports CNN applications in image recognition and lays a foundation for exploring more efficient optimization methods.

Image classification is the most basic task in the field of computer vision, and accuracy is one of the most important metrics for image classification tasks. In the context of the big data era, image data have the characteristics of large data volume and complex and diverse data types. The traditional methods for image classification have a large workload and the accuracy rate is not satisfactory. In order to improve the accuracy of image classification, this paper combines convolutional neural networks and ensemble learning to propose an integrated network architecture. Compared with the base learner before integration, this structure is used to classify the Fashion-MNIST and CIFAR-10 datasets. The accuracy rate increased by 1.171% and 4.527% averagely.

Recently, graph convolutional network (GCN) has drawn increasing attention in hyperspectral image (HSI) classification, as it can process arbitrary non-Euclidean data. However, dynamic GCN that refines the graph heavily relies on the graph embedding in the previous layer, which will result in performance degradation when the embedding contains noise. In this letter, we propose a novel dual residual graph convolutional network (DRGCN) for HSI classification that integrates two adjacency matrices of dual GCN. In detail, one GCN applies a soft adjacency matrix to extract spatial features, the other utilizes the dynamic adjacency matrix to extract global context-aware features. Subsequently, the features extracted by dual GCN are fused to make full use of the complementary and correlated information among two graph representations. Moreover, we introduce residual learning to optimize graph convolutional layers during the training process, to alleviate the over-smoothing problem. The advantage of dual GCN is that it can extract robust and discriminative features from HSI. Extensive experiments on four HSI data sets, including Indian Pines, Pavia University, Salinas, and Houston University, demonstrate the effectiveness and superiority of our proposed DRGCN, even with small-sized training data.

Recently, deep learning methods, for example, convolutional neural networks (CNNs), have achieved high performance in hyperspectral image (HSI) classification. The limited training samples of HSI images make it hard to use deep learning methods with many layers and a large number of convolutional kernels as in large scale imagery tasks, and CNN-based methods usually need long training time. In this paper, we present a wide sliding window and subsampling network (WSWS Net) for HSI classification. It is based on layers of transform kernels with sliding windows and subsampling (WSWS). It can be extended in the wide direction to learn both spatial and spectral features more efficiently. The learned features are subsampled to reduce computational loads and to reduce memorization. Thus, layers of WSWS can learn higher level spatial and spectral features efficiently, and the proposed network can be trained easily by only computing linear weights with least squares. The experimental results show that the WSWS Net achieves excellent performance with different hyperspectral remotes sensing datasets compared with other shallow and deep learning methods. The effects of ratio of training samples, the sizes of image patches, and the visualization of features in WSWS layers are presented.

In hyperspectral image (HSI) classification task, effectively deriving and incorporating spatial information into spectral features is one of a key focus as it can largely influence the performance. Markov random fields (MRFs) are generative and flexible image texture models, and capable of effectively extracting spatial neighbourhood information along multiple spectral wavebands in an unsupervised way. Its parameter estimation process also shares strong compatibility with deep architecture, especially the convolutional neural networks. In this work, we propose an MRF based spectral-spatial fusion network (SSFNet) for HSI classification. Spatial features are extracted using MRF models and further fused with spectral information. Then the proposed SSFNet takes the fused features as input and produces reliable classification results. Comprehensive experiments conducted on the Indian pines and the Pavia university datasets are reported to verify the proposed method.

Hyperspectral images (HSIs) classification methods based on convolutional neural networks are becoming increasingly popular. Many proposed methods extract spatial and spectral features simultaneously, and the interaction between the two types of features leads to unsatisfied classification results. Moreover, most of the existing CNN-based methods mainly consider single-scale, which can cause some important information to be neglected. To address the aforementioned two issues, we propose a Attention-Based Multi-Scale Network (AMSN) for HSIs classification. First, the proposed network is based on 3D-CNN, through channel branch and spatial branch, the AMSN can capture more distinctive spatial and spectral features using different convolution kernels, respectively. Second, Local and global features are extracted by dense network. Then, two features are concatenated to make full use of multi-scale features. Third, attention blocks are used after each branch to achieve the most distinctive features. Experimental results on three HSIs datasets demonstrate that the proposed framework can achieve better classification performance than the several state-of-the-art methods.