Compressed feature vector-based effective object recognition model in detection of COVID-19
•A fast and efficient feature compression method based on compressive sensing is proposed.•A model optimization method is proposed to reduce the computation cost during recognition.•The proposed strategyperforms well on recognition accuracy as well as recognition speed. To better understand the stru...
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| Published in: | Pattern recognition letters Vol. 154; pp. 60 - 67 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.02.2022
Elsevier Science Ltd |
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| ISSN: | 0167-8655, 1872-7344, 0167-8655 |
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| Abstract | •A fast and efficient feature compression method based on compressive sensing is proposed.•A model optimization method is proposed to reduce the computation cost during recognition.•The proposed strategyperforms well on recognition accuracy as well as recognition speed.
To better understand the structure of the COVID-19, and to improve the recognition speed, an effective recognition model based on compressed feature vector is proposed.
Object recognition plays an important role in computer vison aera. To improve the recognition accuracy, most recent approaches always adopt a set of complicated hand-craft feature vectors and build the complex classifiers. Although such approaches achieve the favourable performance on recognition accuracy, they are inefficient. To raise the recognition speed without decreasing the accuracy loss, this paper proposed an efficient recognition modeltrained witha kind of compressed feature vectors. Firstly, we propose a kind of compressed feature vector based on the theory of compressive sensing. A sparse matrix is adopted to compress feature vector from very high dimensions to very low dimensions, which reduces the computation complexity and saves enough information for model training and predicting. Moreover, to improve the inference efficiency during the classification stage, an efficient recognition model is built by a novel optimization approach, which reduces the support vectors of kernel-support vector machine (kernel SVM). The SVM model is established with whether the subject is infected with the COVID-19 as the dependent variable, and the age, gender, nationality, and other factors as independent variables. The proposed approach iteratively builds a compact set of the support vectors from the original kernel SVM, and then the new generated model achieves approximate recognition accuracy with the original kernel SVM. Additionally, with the reduction of support vectors, the recognition time of new generated is greatly improved. Finally, the COVID-19 patients have specific epidemiological characteristics, and the SVM recognition model has strong fitting ability. From the extensive experimental results conducted on two datasets, the proposed object recognition model achieves favourable performance not only on recognition accuracy but also on recognition speed. |
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| AbstractList | To better understand the structure of the COVID-19, and to improve the recognition speed, an effective recognition model based on compressed feature vector is proposed. Object recognition plays an important role in computer vison aera. To improve the recognition accuracy, most recent approaches always adopt a set of complicated hand-craft feature vectors and build the complex classifiers. Although such approaches achieve the favourable performance on recognition accuracy, they are inefficient. To raise the recognition speed without decreasing the accuracy loss, this paper proposed an efficient recognition modeltrained witha kind of compressed feature vectors. Firstly, we propose a kind of compressed feature vector based on the theory of compressive sensing. A sparse matrix is adopted to compress feature vector from very high dimensions to very low dimensions, which reduces the computation complexity and saves enough information for model training and predicting. Moreover, to improve the inference efficiency during the classification stage, an efficient recognition model is built by a novel optimization approach, which reduces the support vectors of kernel-support vector machine (kernel SVM). The SVM model is established with whether the subject is infected with the COVID-19 as the dependent variable, and the age, gender, nationality, and other factors as independent variables. The proposed approach iteratively builds a compact set of the support vectors from the original kernel SVM, and then the new generated model achieves approximate recognition accuracy with the original kernel SVM. Additionally, with the reduction of support vectors, the recognition time of new generated is greatly improved. Finally, the COVID-19 patients have specific epidemiological characteristics, and the SVM recognition model has strong fitting ability. From the extensive experimental results conducted on two datasets, the proposed object recognition model achieves favourable performance not only on recognition accuracy but also on recognition speed. To better understand the structure of the COVID-19, and to improve the recognition speed, an effective recognition model based on compressed feature vector is proposed. Object recognition plays an important role in computer vison aera. To improve the recognition accuracy, most recent approaches always adopt a set of complicated hand-craft feature vectors and build the complex classifiers. Although such approaches achieve the favourable performance on recognition accuracy, they are inefficient. To raise the recognition speed without decreasing the accuracy loss, this paper proposed an efficient recognition modeltrained witha kind of compressed feature vectors. Firstly, we propose a kind of compressed feature vector based on the theory of compressive sensing. A sparse matrix is adopted to compress feature vector from very high dimensions to very low dimensions, which reduces the computation complexity and saves enough information for model training and predicting. Moreover, to improve the inference efficiency during the classification stage, an efficient recognition model is built by a novel optimization approach, which reduces the support vectors of kernel-support vector machine (kernel SVM). The SVM model is established with whether the subject is infected with the COVID-19 as the dependent variable, and the age, gender, nationality, and other factors as independent variables. The proposed approach iteratively builds a compact set of the support vectors from the original kernel SVM, and then the new generated model achieves approximate recognition accuracy with the original kernel SVM. Additionally, with the reduction of support vectors, the recognition time of new generated is greatly improved. Finally, the COVID-19 patients have specific epidemiological characteristics, and the SVM recognition model has strong fitting ability. From the extensive experimental results conducted on two datasets, the proposed object recognition model achieves favourable performance not only on recognition accuracy but also on recognition speed.To better understand the structure of the COVID-19, and to improve the recognition speed, an effective recognition model based on compressed feature vector is proposed. Object recognition plays an important role in computer vison aera. To improve the recognition accuracy, most recent approaches always adopt a set of complicated hand-craft feature vectors and build the complex classifiers. Although such approaches achieve the favourable performance on recognition accuracy, they are inefficient. To raise the recognition speed without decreasing the accuracy loss, this paper proposed an efficient recognition modeltrained witha kind of compressed feature vectors. Firstly, we propose a kind of compressed feature vector based on the theory of compressive sensing. A sparse matrix is adopted to compress feature vector from very high dimensions to very low dimensions, which reduces the computation complexity and saves enough information for model training and predicting. Moreover, to improve the inference efficiency during the classification stage, an efficient recognition model is built by a novel optimization approach, which reduces the support vectors of kernel-support vector machine (kernel SVM). The SVM model is established with whether the subject is infected with the COVID-19 as the dependent variable, and the age, gender, nationality, and other factors as independent variables. The proposed approach iteratively builds a compact set of the support vectors from the original kernel SVM, and then the new generated model achieves approximate recognition accuracy with the original kernel SVM. Additionally, with the reduction of support vectors, the recognition time of new generated is greatly improved. Finally, the COVID-19 patients have specific epidemiological characteristics, and the SVM recognition model has strong fitting ability. From the extensive experimental results conducted on two datasets, the proposed object recognition model achieves favourable performance not only on recognition accuracy but also on recognition speed. •A fast and efficient feature compression method based on compressive sensing is proposed.•A model optimization method is proposed to reduce the computation cost during recognition.•The proposed strategyperforms well on recognition accuracy as well as recognition speed. To better understand the structure of the COVID-19, and to improve the recognition speed, an effective recognition model based on compressed feature vector is proposed. Object recognition plays an important role in computer vison aera. To improve the recognition accuracy, most recent approaches always adopt a set of complicated hand-craft feature vectors and build the complex classifiers. Although such approaches achieve the favourable performance on recognition accuracy, they are inefficient. To raise the recognition speed without decreasing the accuracy loss, this paper proposed an efficient recognition modeltrained witha kind of compressed feature vectors. Firstly, we propose a kind of compressed feature vector based on the theory of compressive sensing. A sparse matrix is adopted to compress feature vector from very high dimensions to very low dimensions, which reduces the computation complexity and saves enough information for model training and predicting. Moreover, to improve the inference efficiency during the classification stage, an efficient recognition model is built by a novel optimization approach, which reduces the support vectors of kernel-support vector machine (kernel SVM). The SVM model is established with whether the subject is infected with the COVID-19 as the dependent variable, and the age, gender, nationality, and other factors as independent variables. The proposed approach iteratively builds a compact set of the support vectors from the original kernel SVM, and then the new generated model achieves approximate recognition accuracy with the original kernel SVM. Additionally, with the reduction of support vectors, the recognition time of new generated is greatly improved. Finally, the COVID-19 patients have specific epidemiological characteristics, and the SVM recognition model has strong fitting ability. From the extensive experimental results conducted on two datasets, the proposed object recognition model achieves favourable performance not only on recognition accuracy but also on recognition speed. |
| Author | Mao, Jinhong Tan, Yi Abaido, Ghada M Liu, Xinzhi Chen, Chao Alsayed, Hamdy |
| Author_xml | – sequence: 1 givenname: Chao surname: Chen fullname: Chen, Chao organization: College of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, China – sequence: 2 givenname: Jinhong surname: Mao fullname: Mao, Jinhong email: jhhmao@hotmail.com organization: Air Force Early Warning Academy, Wuhan 430019, China – sequence: 3 givenname: Xinzhi surname: Liu fullname: Liu, Xinzhi organization: Air Force Early Warning Academy, Wuhan 430019, China – sequence: 4 givenname: Yi surname: Tan fullname: Tan, Yi organization: College of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, China – sequence: 5 givenname: Ghada M surname: Abaido fullname: Abaido, Ghada M organization: Department of Media and Communication Studies, Faculty of Communication, Arts and Sciences, Canadian University Dubai,Dubai, United Arab Emirates – sequence: 6 givenname: Hamdy surname: Alsayed fullname: Alsayed, Hamdy organization: Applied Science University, AI Eker,Kingdom of Bahrain |
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| Snippet | •A fast and efficient feature compression method based on compressive sensing is proposed.•A model optimization method is proposed to reduce the computation... To better understand the structure of the COVID-19, and to improve the recognition speed, an effective recognition model based on compressed feature vector is... |
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| SubjectTerms | Accuracy Complexity Coronaviruses COVID-19 Dependent variables Epidemiology Independent variables Kernels Object recognition Optimization Pattern recognition Sparse matrices Support vector machines |
| Title | Compressed feature vector-based effective object recognition model in detection of COVID-19 |
| URI | https://dx.doi.org/10.1016/j.patrec.2021.12.016 https://www.ncbi.nlm.nih.gov/pubmed/34975183 https://www.proquest.com/docview/2639693683 https://www.proquest.com/docview/2616279523 https://pubmed.ncbi.nlm.nih.gov/PMC8710134 |
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