As a lightweight deep neural network, MobileNet has fewer parameters and higher classification accuracy. In order to further reduce the number of network parameters and improve the classification accuracy, dense blocks that are proposed in DenseNets are introduced into MobileNet. In Dense-MobileNet models, convolution layers with the same size of input feature maps in MobileNet models are taken as dense blocks, and dense connections are carried out within the dense blocks. The new network structure can make full use of the output feature maps generated by the previous convolution layers in dense blocks, so as to generate a large number of feature maps with fewer convolution cores and repeatedly use the features. By setting a small growth rate, the network further reduces the parameters and the computation cost. Two Dense-MobileNet models, Dense1-MobileNet and Dense2-MobileNet, are designed. Experiments show that Dense2-MobileNet can achieve higher recognition accuracy than MobileNet, while only with fewer parameters and computation cost.

/pdf/a-novel-image-classification-approach-via-dense-mobilenet-2089bar60z.pdf

A Novel Image Classification Approach via Dense-MobileNet Models

The low spatial resolution of hyperspectral images leads to the coexistence of multiple ground objects in a single pixel (called mixed pixels). A large number of mixed pixels in a hyperspectral image hinders the subsequent analysis and application of the image. In order to solve this problem, a novel sparse unmixing method, which considers highly similar patches in nonlocal regions of a hyperspectral image, is proposed in this article. This method exploits spectral correlation by using collaborative sparsity regularization and spatial information by employing total variation and weighted nonlocal low-rank tensor regularization. To effectively utilize the tensor decomposition, nonlocal similar patches are first grouped together. Then, these nonlocal patches are stacked to form a patch group tensor. Finally, weighted low-rank tensor regularization is enforced to constrain the patch group to obtain an estimated low-rank abundance image. Experiments on simulated and real hyperspectral datasets validated the superiority of the proposed method in better maintaining fine details and obtaining better unmixing results.

/pdf/weighted-nonlocal-low-rank-tensor-decomposition-method-for-31t7bs1onf.pdf

Weighted Nonlocal Low-Rank Tensor Decomposition Method for Sparse Unmixing of Hyperspectral Images

Change detection methods play an indispensable role in remote sensing. Some change detection methods have obtained a fairly good performance by introducing attention mechanism on the basis of the convolutional neural network (CNN), but identifying intricate changes remains difficult. In response to these problems, this article proposes a new model for detecting changes in remote sensing, namely, MTCNet, which combines the advantages of multi-scale Transformer with the convolutional block attention module (CBAM) to improve the detection quality of different remote sensing images. On the basis of traditional convolutions, the Transformer module is introduced to extract bitemporal image features by modeling contextual information. Based on the Transformer module, a multi-scale module is designed to form a multi-scale Transformer, which can obtain features at different scales in bitemporal images, thereby identifying the changes we are interested in. Based on the multiscale Transformer module, the CBAM is introduced. The CBAM is split into a spatial attention module (SAM) and a channel attention module (CAM), which are applied to the front and back ends of the multi-scale Transformer respectively. Spatial information and channel information of feature maps are modeled separately. In this article, the validity and efficiency of the method are verified by a large number of experiments on the LEVIR-CD dataset and the WHU-CD dataset. 

https://ieeexplore.ieee.org/ielx7/4609443/4609444/09855775.pdf

A CBAM Based Multiscale Transformer Fusion Approach for Remote Sensing Image Change Detection

Microorganisms or microbes comprise majority of the diversity on earth and are extremely important to human life. They are also integral to processes in the ecosystem. The process of their recognition is highly tedious, but very much essential in microbiology to carry out different experimentation. To overcome certain challenges, machine learning techniques assist microbiologists in automating the entire process. This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. This review investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time. In addition to this, this paper also addresses the certain challenges faced by researchers in this field. Total of 100 research publications in the chronological order of their appearance have been considered for the time period 1995-2021. This review will be extremely beneficial to the researchers due to the detailed analysis of different methodologies and comprehensive overview of effectiveness of different ML techniques being applied in microorganism image recognition field.

/pdf/machine-learning-and-deep-learning-based-computational-3qvtzg2hzg.pdf

Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

Colonic polyps are more likely to be cancerous, especially those with large diameter, large number and atypical hyperplasia. If colonic polyps cannot be treated in early stage, they are likely to develop into colon cancer. Colonoscopy is easily limited by the operator’s experience, and factors such as inexperience and visual fatigue will directly affect the accuracy of diagnosis. Cooperating with Hunan children’s hospital, we proposed and improved a deep learning approach with global average pooling (GAP) in colonoscopy for assisted diagnosis. Our approach for assisted diagnosis in colonoscopy can prompt endoscopists to pay attention to polyps that may be ignored in real time, improve the detection rate, reduce missed diagnosis, and improve the efficiency of medical diagnosis. We selected colonoscopy images from the gastrointestinal endoscopy room of Hunan children’s hospital to form the colonic polyp datasets. And we applied the image classification method based on Deep Learning to the classification of Colonic Polyps. The classic networks we used are VGGNets and ResNets. By using global average pooling, we proposed the improved approaches: VGGNets-GAP and ResNets-GAP. The accuracies of all models in datasets exceed 98%. The TPR and TNR are above 96 and 98% respectively. In addition, VGGNets-GAP networks not only have high classification accuracies, but also have much fewer parameters than those of VGGNets. The experimental results show that the proposed approach has good effect on the automatic detection of colonic polyps. The innovations of our method are in two aspects: (1) the detection accuracy of colonic polyps has been improved. (2) our approach reduces the memory consumption and makes the model lightweight. Compared with the original VGG networks, the parameters of our VGG19-GAP networks are greatly reduced.

/pdf/an-improved-deep-learning-approach-and-its-applications-on-3htf6m9082.pdf

An improved deep learning approach and its applications on colonic polyp images detection

An image object recognition approach based on deep features and adaptive weighted joint sparse representation (D-AJSR) is proposed in this paper. D-AJSR is a data-lightweight classification framework, which can classify and recognize objects well with few training samples. In D-AJSR, the convolutional neural network (CNN) is used to extract the deep features of the training samples and test samples. Then, we use the adaptive weighted joint sparse representation to identify the objects, in which the eigenvectors are reconstructed by calculating the contribution weights of each eigenvector. Aiming at the high-dimensional problem of deep features, we use the principal component analysis (PCA) method to reduce the dimensions. Lastly, combined with the joint sparse model, the public features and private features of images are extracted from the training sample feature set so as to construct the joint feature dictionary. Based on the joint feature dictionary, sparse representation-based classifier (SRC) is used to recognize the objects. Experiments on face images and remote sensing images show that D-AJSR is superior to the traditional SRC method and some other advanced methods.

/pdf/image-object-recognition-via-deep-feature-based-adaptive-e22l2m5ryi.pdf

Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation

In recent years, more and more attention has been paid to single image super-resolution reconstruction (SISR) by using deep learning networks. These networks have achieved good reconstruction results, but how to make better use of the feature information in the image, how to improve the network convergence speed, and so on still need further study. According to the above problems, a novel deep residual dense network (DRDN) is proposed in this paper. In detail, DRDN uses the residual-dense structure for local feature fusion, and finally carries out global residual fusion reconstruction. Residual-dense connection can make full use of the features of low-resolution images from shallow to deep layers, and provide more low-resolution image information for super-resolution reconstruction. Multi-hop connection can make errors spread to each layer of the network more quickly, which can alleviate the problem of difficult training caused by deepening network to a certain extent. The experiments show that DRDN not only ensure good training stability and successfully converge but also has less computing cost and higher reconstruction efficiency.

An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution

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Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation

An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution