Chinese medical question answer selection via hybrid models based on CNN and GRU

Zhang, Yuteng; Lü, Wenpeng; Ou, Weihua; Zhang, Guoqiang; Zhang, Xu; Cheng, Jinyong; Zhang, Weiyu

doi:10.1007/s11042-019-7240-1

Cited by 46 publications

(25 citation statements)

References 27 publications

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“…In this experiment, the first face images are respectively taken as training set, and the rest as test set. The optimal neighbor number and balance coefficients ( 1,2,3,4) Based on the results in Table 3, we can see that the proposed RME algorithm perform better than MSEC, CRC, FME, MDRL, and SOSI algorithms irrespective of the variation of training sample size. With one training sample for each object, the recognition rates of six compared algorithms are 43.67%, 43.97%, 47.50%, 47.12%, 46.53%, and 50.58%, respectively.…”

Section: Experiments On Feret Face Databasementioning

confidence: 96%

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Robust Manifold Embedding for Face Recognition

Xiang

Shi

et al. 2020

IEEE Access

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Flexible manifold embedding (FME) has been recognized as an effective method for face recognition by integrating both class label information from labeled data and manifold structure information of all data. In order to achieve better performance, this particular method usually requires sufficient samples to make manifold smooth. However, it is often hard to provide enough samples for FME in practice. In view of facial symmetry, we utilize left/right mirror face images to address the deficiency of samples in manifold embedding. These mirror images enable to reflect variations of illuminations, or poses or both them that the original face images cannot provide. Therefore, we propose a robust manifold embedding (RME) algorithm in this paper, which can fully use the class label information and correctly capture the underlying manifold structure. The proposed RME algorithm integrates two complementary characteristics of the label fitness and the manifold smoothness. Moreover, the original face images and its left/right mirror images are jointly used in the learning of RME, which shows better robustness against the variations of both illuminations and poses. Extensive experiments on several public face databases demonstrate that the proposed RME algorithm is promising for higher recognition accuracy than other compared methods in reference. INDEX TERMS manifold embedding; mirror image; robust manifold embedding (RME); face recognition

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Section: Experiments On Feret Face Databasementioning

confidence: 96%

“…Dimension reduction is a hot topic for the recognition tasks of high dimensional image. In past decades, a large number of dimension reduction algorithms have been proposed [1][2][3][4][5][6][7]. Linear discriminant analysis (LDA) and principal component analysis (PCA) are the two most classical algorithms for dimension reduction.…”

Section: Introductionmentioning

confidence: 99%

Robust Manifold Embedding for Face Recognition

Xiang

Shi

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The application field of convolutional neural network is quite extensive, such as image recognition [18][19][20], image classification [21][22][23][24], target tracking [25][26][27][28], text analysis [29][30][31][32], target detection, and image retrieval [33,34]. It is a powerful tool for image processing and research.…”

Section: Semantic Segmentation Based On Convolutional Neural Networkmentioning

confidence: 99%

A video coverless information hiding algorithm based on semantic segmentation

Pan

Qin

Tan

et al. 2020

J Image Video Proc.

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Due to the fact that coverless information hiding can effectively resist the detection of steganalysis tools, it has attracted more attention in the field of information hiding. At present, most coverless information hiding schemes select text and image as transmission carriers, while there are few studies on emerging popular media such as video, which has more abundant contents. Taking the natural video as the carrier is more secure and can avoid the attention of attackers. In this paper, we propose a coverless video steganography algorithm based on semantic segmentation. Specifically, to establish the mapping relationship between secret information and video files effectively, this paper introduces the deep learning based on semantic segmentation network to calculate the statistical histogram of semantic information. To quickly index the sender's secret message to the corresponding video frame, we build a three-digit index structure. The receiver can extract the valid video frame from the three-digit index information and restore the secret information. On the one hand, the neural network is trained through the original image and the noisy image in this scheme; therefore, it can not only effectively resist the interference of noises, but also accurately extract the robust deep features of the image. The frames of video generate the robust mapping to the secret information after the semantic information statistics. On the other hand, semantic segmentation belongs to pixel-level segmentation, which has high requirements for network parameters, so it is difficult for attackers to decrypt and recover secret information. Since this scheme does not modify the primitiveness of video data, it can effectively resist steganalysis tools. The experimental results and analysis show that the video coverless information hiding scheme has a large capacity and a certain resistance to noise attack.

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“…Within the deep learning method [5] in the field of recognition, target recognition has achieved significant results. Deep learning simulates the working mechanism of the human brain and analyzes and interprets data by using a multilayer nonlinear neural network [6], [7]. Although deep learning models have strong feature description ability in many pattern recognition tasks, such as the deep convolution neural network [8] and residual network model proposed by Fan et al [9], they exhibit better performance.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Algorithm Based on Correlation Coefficient and Ensemble-Augmented Sparsity

Cheng

2020

IEEE Access

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The representation-based classification method has become a research hotspot in recent years. Representation-based classifiers assign class labels directly to test samples based on a structured dictionary. The structured dictionary is composed of training samples. We call the samples in the dictionary as atoms. To further improve the expression ability of different training atoms in the classifier to the test samples, we propose an ensemble-enhanced sparse classification algorithm based on the correlation coefficient. The model proceeds from two aspects: the representation coefficient level and the similarity between different samples. First, a sparse dictionary is combined with the multiplicative property of training samples to build and solve the sparse and collaborative representation algorithm. The fusion representation coefficients are obtained by weighted sparse representation coefficients and collaborative representation coefficients. The test samples are reconstructed by fusing the representation coefficients, and the minimization recovery residuals for each class of samples are calculated. Second, the correlation coefficient value between the test sample and the training sample is calculated, and the maximum correlation quotient between the test sample and the training sample is obtained. Finally, the maximum correlation quotient and the minimization recovery residual are weighted, and the decision classification is carried out in the decision function to achieve the final face recognition. Experiments on AR, Extended Yale B, Georgia Tech and other general face databases show the effectiveness of the algorithm. The main contribution of this model is that it is more robust than a single sparse or collaborative representation model and can improve recognition accuracy.

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Chinese medical question answer selection via hybrid models based on CNN and GRU

Cited by 46 publications

References 27 publications

Robust Manifold Embedding for Face Recognition

Robust Manifold Embedding for Face Recognition

A video coverless information hiding algorithm based on semantic segmentation

Face Recognition Algorithm Based on Correlation Coefficient and Ensemble-Augmented Sparsity

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