Co-Saliency Detection Via Unified Hierarchical Graph Neural Network With Geometric Attention

2021 ◽  
Author(s):  
Jiaqing Qiao ◽  
Shaowei Sun ◽  
Mingzhu Xu ◽  
Yongqiang Li ◽  
Bing Liu
Keyword(s):  
Neural Network ◽  
Saliency Detection ◽  
Hierarchical Graph

A Novel Method of Maize Leaf Disease Image Identification Based on a Multichannel Convolutional Neural Network

2018 ◽  
Vol 61 (5) ◽  
pp. 1461-1474 ◽  
Author(s):  
Zhongqi Lin ◽  
Shaomin Mu ◽  
Aiju Shi ◽  
Chao Pang ◽  
Xiaoxiao Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Graphics Processing Units ◽  
High Efficiency ◽  
Saliency Detection ◽  
Machine Learning Algorithms ◽  
Brown Spot ◽  
Maize Leaf ◽  
Leaf Disease ◽  
Fully Connected

Abstract. Traditional methods for detecting maize leaf diseases (such as leaf blight, sooty blotch, brown spot, rust, and purple leaf sheaf) are typically labor-intensive and strongly subjective. With the aim of achieving high accuracy and efficiency in the identification of maize leaf diseases from digital imagery, this article proposes a novel multichannel convolutional neural network (MCNN). The MCNN is composed of an input layer, five convolutional layers, three subsampling layers, three fully connected layers, and an output layer. Using a method that imitates human visual behavior in video saliency detection, the first and second subsampling layers are connected directly with the first fully connected layer. In addition, the mixed modes of pooling and normalization methods, rectified linear units (ReLU), and dropout are introduced to prevent overfitting and gradient diffusion. The learning process corresponding to the network structure is also illustrated. At present, there are no large-scale images of maize leaf disease for use as experimental samples. To test the proposed MCNN, 10,820 RGB images containing five types of disease were collected from maize planting areas in Shandong Province, China. The original images could not be used directly in identification experiments because of noise and irrelevant regions. They were therefore denoised and segmented by homomorphic filtering and region of interest (ROI) segmentation to construct a standard database. A series of experiments on 8 GB graphics processing units (GPUs) showed that the MCNN could achieve an average accuracy of 92.31% and a high efficiency in the identification of maize leaf diseases. The multichannel design and the integration of different innovations proved to be helpful methods for boosting performance. Keywords: Artificial intelligence, Convolutional neural network, Deep learning, Image classification, Machine learning algorithms, Maize leaf disease.

scholarly journals A Deep Multiscale Fusion Method via Low-Rank Sparse Decomposition for Object Saliency Detection Based on Urban Data in Optical Remote Sensing Images

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Cheng Zhang ◽  
Dan He
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Saliency Detection ◽  
Saliency Map ◽  
Low Rank ◽  
Optical Remote Sensing ◽  
Fusion Method ◽  
Remote Sensing Images ◽  
Sparse Decomposition ◽  
Depth Feature

The urban data provides a wealth of information that can support the life and work for people. In this work, we research the object saliency detection in optical remote sensing images, which is conducive to the interpretation of urban scenes. Saliency detection selects the regions with important information in the remote sensing images, which severely imitates the human visual system. It plays a powerful role in other image processing. It has successfully made great achievements in change detection, object tracking, temperature reversal, and other tasks. The traditional method has some disadvantages such as poor robustness and high computational complexity. Therefore, this paper proposes a deep multiscale fusion method via low-rank sparse decomposition for object saliency detection in optical remote sensing images. First, we execute multiscale segmentation for remote sensing images. Then, we calculate the saliency value, and the proposal region is generated. The superpixel blocks of the remaining proposal regions of the segmentation map are input into the convolutional neural network. By extracting the depth feature, the saliency value is calculated and the proposal regions are updated. The feature transformation matrix is obtained based on the gradient descent method, and the high-level semantic prior knowledge is obtained by using the convolutional neural network. The process is iterated continuously to obtain the saliency map at each scale. The low-rank sparse decomposition of the transformed matrix is carried out by robust principal component analysis. Finally, the weight cellular automata method is utilized to fuse the multiscale saliency graphs and the saliency map calculated according to the sparse noise obtained by decomposition. Meanwhile, the object priors knowledge can filter most of the background information, reduce unnecessary depth feature extraction, and meaningfully improve the saliency detection rate. The experiment results show that the proposed method can effectively improve the detection effect compared to other deep learning methods.

scholarly journals A novel approach to reconstruction based saliency detection via convolutional neural network stacked with auto-encoder

2019 ◽  
Vol 349 ◽  
pp. 145-155 ◽  
Author(s):  
Xinchen Lin ◽  
Yang Tang ◽  
Huaglory Tianfield ◽  
Feng Qian ◽  
Weimin Zhong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Saliency Detection ◽  
Novel Approach
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