An Intelligent Image Feature Recognition Algorithm With Hierarchical Attribute Constraints Based on Weak Supervision and Label Correlation

Zou, Songshang; Chen, Hao; Zhou, Hao; Chen, Jianguo

doi:10.1109/access.2020.2998164

Cited by 7 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fourth step is to normalize the gradient direction histogram in the block in the block unit. The specific operation is to organize adjacent t * t cells into a block, connect the gradient direction histograms in the cells in series to obtain the gradient direction histogram of the block, and then normalize the block [12][13][14][15].…”

Section: Natural Feature Recognition Of Multi Pose Face Images Based ...mentioning

confidence: 99%

Natural Feature Recognition of Multi Pose Face Images based on Augmented Reality

Ge¹,

Zhang²,

Wang³

et al. 2022

jist

View full text Add to dashboard Cite

Traditional face feature recognition methods have a few problems, such as low accuracy of image feature recognition and high time consumption of image feature recognition. This paper introduces augmented reality technology to realize natural feature recognition of multi pose face images. The multi pose features of face image are obtained by augmented reality technology, the color normalization of face image is realized by gamma algorithm, and the natural features of multi pose face image are extracted by three-dimensional registration technology. The feature error reconstruction of multi pose face image is realized according to sparse representation, the face image samples are projected into the optimal discriminant vector space by using two-dimensional linear discriminant analysis algorithm, and the natural feature recognition of multi pose face image is completed according to augmented reality technology. The experimental results show that the comprehensive accuracy of facial image expression recognition is as high as 95%, and the average error rate of natural feature recognition is only 3%; The time taken for feature recognition is only 3.5s, which shows that the method used in this study has higher efficiency of natural feature recognition of face image.

show abstract

Section: Natural Feature Recognition Of Multi Pose Face Images Based ...mentioning

confidence: 99%

Natural Feature Recognition of Multi Pose Face Images based on Augmented Reality

Ge¹,

Zhang²,

Wang³

et al. 2022

jist

View full text Add to dashboard Cite

show abstract

“…For instance, external environmental variables might cause shifts in the spectral signatures of consistent ground entities. Conversely, unrelated terrestrial groups might share similar spectral patterns, especially when contamination exists between neighboring areas, complicating accurate object classification [11]. Hence, an excessive reliance on merely spectral characteristics is likely to result in the misclassification of the intended subjects [12].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Convolutional Network Combining Multiscale 3D Depthwise Separable Convolution and CBAM Residual Dilated Convolution for Hyperspectral Image Classification

Hu,

Tian,

2023

Remote Sensing

View full text Add to dashboard Cite

In recent years, convolutional neural networks (CNNs) have been increasingly leveraged for the classification of hyperspectral imagery, displaying notable advancements. To address the issues of insufficient spectral and spatial information extraction and high computational complexity in hyperspectral image classification, we introduce the MDRDNet, an integrated neural network model. This novel architecture is comprised of two main components: a Multiscale 3D Depthwise Separable Convolutional Network and a CBAM-augmented Residual Dilated Convolutional Network. The first component employs depthwise separable convolutions in a 3D setting to efficiently capture spatial–spectral characteristics, thus substantially reducing the computational burden associated with 3D convolutions. Meanwhile, the second component enhances the network by integrating the Convolutional Block Attention Module (CBAM) with dilated convolutions via residual connections, effectively counteracting the issue of model degradation. We have empirically evaluated the MDRDNet’s performance by running comprehensive experiments on three publicly available datasets: Indian Pines, Pavia University, and Salinas. Our findings indicate that the overall accuracy of the MDRDNet on the three datasets reached 98.83%, 99.81%, and 99.99%, respectively, which is higher than the accuracy of existing models. Therefore, the MDRDNet proposed in this study can fully extract spatial–spectral joint information, providing a new idea for solving the problem of large model calculations in 3D convolutions.

show abstract

“…In recent decades, the rapid development of satellite remote sensing technology has provided new options for the classification of winter wheat. Classifying different cultivars of winter wheat is essentially a task about classifying features with weak intra-class variation in remote sensing imagery classification [10], since different cultivars of winter wheat are the sub-category of wheat, and they have similar phenotypes and consistent texture features [11]. Many studies used satellite data, such as Landsat, Modis, Sentinel, etc., to do the crop classification due to the wide coverage and low cost [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery

Lyu,

Du,

Zhang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Crop phenotype observation techniques via UAV (unmanned aerial vehicle) are necessary to identify different winter wheat cultivars to better realize their future smart productions and satisfy the requirement of smart agriculture. This study proposes a UAV-based hyperspectral remote sensing system for the fine classification of different winter wheat cultivars. Firstly, we set 90% heading overlap and 85% side overlap as the optimal flight parameters, which can meet the requirements of following hyperspectral imagery mosaicking and spectral stitching of different winter wheat cultivars areas. Secondly, the mosaicking algorithm of UAV hyperspectral imagery was developed, and the correlation coefficient of stitched spectral curves before and after mosaicking reached 0.97, which induced this study to extract the resultful spectral curves of six different winter wheat cultivars. Finally, the hyperspectral imagery dimension reduction experiments were compared with principal component analysis (PCA), minimum noise fraction rotation (MNF), and independent component analysis (ICA); the winter wheat cultivars classification experiments were compared with support vector machines (SVM), maximum likelihood estimate (MLE), and U-net neural network ENVINet5 model. Different dimension reduction methods and classification methods were compared to get the best combination for classification of different winter wheat cultivars. The results show that the mosaicked hyperspectral imagery effectively retains the original spectral feature information, and type 4 and type 6 winter wheat cultivars have the best classification results with the classification accuracy above 84%. Meanwhile, there is a 30% improvement in classification accuracy after dimension reduction, the MNF dimension reduction combined with ENVINet5 classification result is the best, its overall accuracy and Kappa coefficients are 83% and 0.81, respectively. The results indicate that the UAV-based hyperspectral remote sensing system can potentially be used for classifying different cultivars of winter wheat, and it provides a reference for the classification of crops with weak intra-class differences.

show abstract

An Intelligent Image Feature Recognition Algorithm With Hierarchical Attribute Constraints Based on Weak Supervision and Label Correlation

Cited by 7 publications

References 30 publications

Natural Feature Recognition of Multi Pose Face Images based on Augmented Reality

Natural Feature Recognition of Multi Pose Face Images based on Augmented Reality

Hybrid Convolutional Network Combining Multiscale 3D Depthwise Separable Convolution and CBAM Residual Dilated Convolution for Hyperspectral Image Classification

Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery

Contact Info

Product

Resources

About