Uncertainty-optimized deep learning model for small-scale person re-identification

Zhao, Cairong; Chen, Kang; Zang, Di; Zhang, Zhaoxiang; Zuo, Wangmeng; Miao, Duoqian

doi:10.1007/s11432-019-2675-3

Cited by 24 publications

(3 citation statements)

References 47 publications

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“…Deep learning has come to the fore in recent years as an artificial intelligence approach that provides successful results in many image processing applications from image enhancement (such as [ 52 ]) to object identification (such as [ 53 , 54 ]).…”

Section: Methodsmentioning

confidence: 99%

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks

Yaşar

Ceylan

2020

Appl Intell

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In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelligence method, was used, and automatic classification of images was performed using convolutional neural networks (CNN). In the first training-test data set used in the study, there were 230 X-ray images, of which 150 were Covid-19 and 80 were non-Covid-19, while in the second training-test data set there were 476 X-ray images, of which 150 were Covid-19 and 326 were non-Covid-19. Thus, classification results have been provided for two data sets, containing predominantly Covid-19 images and predominantly non-Covid-19 images, respectively. In the study, a 23-layer CNN architecture and a 54-layer CNN architecture were developed. Within the scope of the study, the results were obtained using chest X-ray images directly in the training-test procedures and the sub-band images obtained by applying dual tree complex wavelet transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying local binary pattern (LBP) to the chest X-ray images. Within the scope of the study, four new result generation pipeline algorithms having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in this study, the training sessions were carried out using the k-fold cross validation method. Here the k value was chosen as 23 for the first and second training-test data sets. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy, F-1 score, and area under the receiver operating characteristic curve (AUC) for the first training-test data set were 0,9947, 0,9800, 0,9843, 0,9881 and 0,9990 respectively; while for the second training-test data set, they were 0,9920, 0,9939, 0,9891, 0,9828 and 0,9991; respectively. Within the scope of the study, finally, all the images were combined and the training and testing processes were repeated for a total of 556 X-ray images comprising 150 Covid-19 images and 406 non-Covid-19 images, by applying 2-fold cross. In this context, the average highest values of sensitivity, specificity, accuracy, F-1 score, and AUC for this last training-test data set were found to be 0,9760, 1,0000, 0,9906, 0,9823 and 0,9997; respectively.

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Section: Methodsmentioning

confidence: 99%

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks

Yaşar

Ceylan

2020

Appl Intell

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“…The process of choosing or creating a proper set of features is called feature extraction [21]. The literature presents many techniques for attribute extraction such as: PCA [22], kernel PCA [23], isomap [24], or deep neural networks [20,25,26].…”

Section: Related Workmentioning

confidence: 99%

Linear Classifier Combination via Multiple Potential Functions

Trajdos,

Burduk

2020

Preprint

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A vital aspect of the classification based model construction process is the calibration of the scoring function. One of the weaknesses of the calibration process is that it does not take into account the information about the relative positions of the recognized objects in the feature space. To alleviate this limitation, in this paper, we propose a novel concept of calculating a scoring function based on the distance of the object from the decision boundary and its distance to the class centroid. An important property is that the proposed score function has the same nature for all linear base classifiers, which means that outputs of these classifiers are equally represented and have the same meaning. The proposed approach is compared with other ensemble algorithms and experiments on multiple Keel datasets demonstrate the effectiveness of our method. To discuss the results of our experiments, we use multiple classification performance measures and statistical analysis.

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“…Nevertheless, these studies are proposed for interactive annotation for a single image rather than annotating images in batch. To exploit the informative images in the wild, researchers have introduced active learning [5], semi-supervised learning [6], uncertainty learning [7], incremental learning [8], context learning [9] and self-supervised learning [1] for model enhancement. To sum up, we wonder whether we can annotate the unlabeled image with the least human labor and train a state-of-the-art segmentation model using the least data.…”

mentioning

confidence: 99%

Human-in-the-loop image segmentation and annotation

Zhang

Wang

Xie

et al. 2020

Sci. China Inf. Sci.

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Uncertainty-optimized deep learning model for small-scale person re-identification

Abstract: Mobile person re-identification with a lightweight trident CNN SCIENCE CHINA Information Sciences 63, 219102 (2020); Weight-based sparse coding for multi-shot person re-identification SCIENCE CHINA Information Sciences 58, 100104 (2015); SCIENCE CHINA Information Sciences

Cited by 24 publications

References 47 publications

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks

Linear Classifier Combination via Multiple Potential Functions

Human-in-the-loop image segmentation and annotation

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