Video Genre Classification Using Weighted Kernel Logistic Regression
Due to the widening semantic gap of videos, computational tools to classify these videos into different genre are highly needed to narrow it. Classifying videos accurately demands good representation of video data and an efficient and effective model to carry out the classification task. Kernel Logi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2013/653687 |
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| _version_ | 1832559310163410944 |
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| author | Ahmed A. M. Hamed Renfa Li Zhang Xiaoming Cheng Xu |
| author_facet | Ahmed A. M. Hamed Renfa Li Zhang Xiaoming Cheng Xu |
| author_sort | Ahmed A. M. Hamed |
| collection | DOAJ |
| description | Due to the widening semantic gap of videos, computational tools to classify these videos into different genre are highly needed to narrow it. Classifying videos accurately demands good representation of video data and an efficient and effective model to carry out the classification task. Kernel Logistic Regression (KLR), kernel version of logistic regression (LR), proves its efficiency as a classifier, which can naturally provide probabilities and extend to multiclass classification problems. In this paper, Weighted Kernel Logistic Regression (WKLR) algorithm is implemented for video genre classification to obtain significant accuracy, and it shows accurate and faster good results. |
| format | Article |
| id | doaj-art-ccf668f98def47e380627af78b4ab09d |
| institution | Kabale University |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-ccf668f98def47e380627af78b4ab09d2025-02-03T01:30:28ZengWileyAdvances in Multimedia1687-56801687-56992013-01-01201310.1155/2013/653687653687Video Genre Classification Using Weighted Kernel Logistic RegressionAhmed A. M. Hamed0Renfa Li1Zhang Xiaoming2Cheng Xu3Hunan University, Changsha 410082, ChinaHunan University, Changsha 410082, ChinaHunan University, Changsha 410082, ChinaHunan University, Changsha 410082, ChinaDue to the widening semantic gap of videos, computational tools to classify these videos into different genre are highly needed to narrow it. Classifying videos accurately demands good representation of video data and an efficient and effective model to carry out the classification task. Kernel Logistic Regression (KLR), kernel version of logistic regression (LR), proves its efficiency as a classifier, which can naturally provide probabilities and extend to multiclass classification problems. In this paper, Weighted Kernel Logistic Regression (WKLR) algorithm is implemented for video genre classification to obtain significant accuracy, and it shows accurate and faster good results.http://dx.doi.org/10.1155/2013/653687 |
| spellingShingle | Ahmed A. M. Hamed Renfa Li Zhang Xiaoming Cheng Xu Video Genre Classification Using Weighted Kernel Logistic Regression Advances in Multimedia |
| title | Video Genre Classification Using Weighted Kernel Logistic Regression |
| title_full | Video Genre Classification Using Weighted Kernel Logistic Regression |
| title_fullStr | Video Genre Classification Using Weighted Kernel Logistic Regression |
| title_full_unstemmed | Video Genre Classification Using Weighted Kernel Logistic Regression |
| title_short | Video Genre Classification Using Weighted Kernel Logistic Regression |
| title_sort | video genre classification using weighted kernel logistic regression |
| url | http://dx.doi.org/10.1155/2013/653687 |
| work_keys_str_mv | AT ahmedamhamed videogenreclassificationusingweightedkernellogisticregression AT renfali videogenreclassificationusingweightedkernellogisticregression AT zhangxiaoming videogenreclassificationusingweightedkernellogisticregression AT chengxu videogenreclassificationusingweightedkernellogisticregression |