scholarly journals A Novel Approach for Video Text Detection and Recognition Based on a Corner Response Feature Map and Transferred Deep Convolutional Neural Network

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 40198-40211 ◽  
Author(s):  
Wei Lu ◽  
Hongbo Sun ◽  
Jinghui Chu ◽  
Xiangdong Huang ◽  
Jiexiao Yu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Text Detection ◽  
Deep Convolutional Neural Network ◽  
Feature Map ◽  
Novel Approach ◽  
Video Text Detection ◽  
Response Feature ◽  
Detection And Recognition

scholarly journals Apple Leaf Disease Identification Through Region-of-Interest-Aware Deep Convolutional Neural Network

2020 ◽  
Vol 64 (2) ◽  
pp. 20507-1-20507-10 ◽  
Author(s):  
Hee-Jin Yu ◽  
Chang-Hwan Son ◽  
Dong Hyuk Lee
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Leaf Area ◽  
Region Of Interest ◽  
Deep Convolutional Neural Network ◽  
Discriminative Power ◽  
Feature Map ◽  
Traditional Approaches

Abstract Traditional approaches for the identification of leaf diseases involve the use of handcrafted features such as colors and textures for feature extraction. Therefore, these approaches may have limitations in extracting abundant and discriminative features. Although deep learning approaches have been recently introduced to overcome the shortcomings of traditional approaches, existing deep learning models such as VGG and ResNet have been used in these approaches. This indicates that the approach can be further improved to increase the discriminative power because the spatial attention mechanism to predict the background and spot areas (i.e., local areas with leaf diseases) has not been considered. Therefore, a new deep learning architecture, which is hereafter referred to as region-of-interest-aware deep convolutional neural network (ROI-aware DCNN), is proposed to make deep features more discriminative and increase classification performance. The primary idea is that leaf disease symptoms appear in leaf area, whereas the background region does not contain useful information regarding leaf diseases. To realize this, two subnetworks are designed. One subnetwork is the ROI subnetwork to provide more discriminative features from the background, leaf areas, and spot areas in the feature map. The other subnetwork is the classification subnetwork to increase the classification accuracy. To train the ROI-aware DCNN, the ROI subnetwork is first learned with a new image set containing the ground truth images where the background, leaf area, and spot area are divided. Subsequently, the entire network is trained in an end-to-end manner to connect the ROI subnetwork with the classification subnetwork through a concatenation layer. The experimental results confirm that the proposed ROI-aware DCNN can increase the discriminative power by predicting the areas in the feature map that are more important for leaf diseases identification. The results prove that the proposed method surpasses conventional state-of-the-art methods such as VGG, ResNet, SqueezeNet, bilinear model, and multiscale-based deep feature extraction and pooling.

scholarly journals Multidimensional Face Representation in a Deep Convolutional Neural Network Reveals the Mechanism Underlying AI Racism

2021 ◽  
Vol 15 ◽  
Author(s):  
Jinhua Tian ◽  
Hailun Xie ◽  
Siyuan Hu ◽  
Jia Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Underlying Mechanism ◽  
Deep Convolutional Neural Network ◽  
The Other ◽  
Training Data ◽  
Identification Accuracy ◽  
Social Bias ◽  
Novel Approach ◽  
Ai Ethics

The increasingly popular application of AI runs the risk of amplifying social bias, such as classifying non-white faces as animals. Recent research has largely attributed this bias to the training data implemented. However, the underlying mechanism is poorly understood; therefore, strategies to rectify the bias are unresolved. Here, we examined a typical deep convolutional neural network (DCNN), VGG-Face, which was trained with a face dataset consisting of more white faces than black and Asian faces. The transfer learning result showed significantly better performance in identifying white faces, similar to the well-known social bias in humans, the other-race effect (ORE). To test whether the effect resulted from the imbalance of face images, we retrained the VGG-Face with a dataset containing more Asian faces, and found a reverse ORE that the newly-trained VGG-Face preferred Asian faces over white faces in identification accuracy. Additionally, when the number of Asian faces and white faces were matched in the dataset, the DCNN did not show any bias. To further examine how imbalanced image input led to the ORE, we performed a representational similarity analysis on VGG-Face's activation. We found that when the dataset contained more white faces, the representation of white faces was more distinct, indexed by smaller in-group similarity and larger representational Euclidean distance. That is, white faces were scattered more sparsely in the representational face space of the VGG-Face than the other faces. Importantly, the distinctiveness of faces was positively correlated with identification accuracy, which explained the ORE observed in the VGG-Face. In summary, our study revealed the mechanism underlying the ORE in DCNNs, which provides a novel approach to studying AI ethics. In addition, the face multidimensional representation theory discovered in humans was also applicable to DCNNs, advocating for future studies to apply more cognitive theories to understand DCNNs' behavior.

scholarly journals Multidimensional face representation in deep convolutional neural network reveals the mechanism underlying AI racism

2020 ◽  
Author(s):  
Jinhua Tian ◽  
Hailun Xie ◽  
Siyuan Hu ◽  
Jia Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Underlying Mechanism ◽  
Deep Convolutional Neural Network ◽  
The Other ◽  
Identification Accuracy ◽  
Social Bias ◽  
Novel Approach ◽  
The Face ◽  
Ai Ethics

AbstractThe increasingly popular application of AI runs the risks of amplifying social bias, such as classifying non-white faces to animals. Recent research has attributed the bias largely to data for training. However, the underlying mechanism is little known, and therefore strategies to rectify the bias are unresolved. Here we examined a typical deep convolutional neural network (DCNN), VGG-Face, which was trained with a face dataset consisting of more white faces than black and Asian faces. The transfer learning result showed significantly better performance in identifying white faces, just like the well-known social bias in human, the other-race effect (ORE). To test whether the effect resulted from the imbalance of face images, we retrained the VGG-Face with a dataset containing more Asian faces, and found a reverse ORE that the newly-trained VGG-Face preferred Asian faces over white faces in identification accuracy. In addition, when the number of Asian faces and white faces were matched in the dataset, the DCNN did not show any bias. To further examine how imbalanced image input led to the ORE, we performed the representational similarity analysis on VGG-Face’s activation. We found that when the dataset contained more white faces, the representation of white faces was more distinct, indexed by smaller ingroup similarity and larger representational Euclidean distance. That is, white faces were scattered more sparsely in the representational face space of the VGG-Face than the other faces. Importantly, the distinctiveness of faces was positively correlated with the identification accuracy, which explained the ORE observed in the VGG-Face. In sum, our study revealed the mechanism underlying the ORE in DCNNs, which provides a novel approach of study AI ethics. In addition, the face multidimensional representation theory discovered in human was found also applicable to DCNNs, advocating future studies to apply more cognitive theories to understand DCNN’s behavior.

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