Multimodal Features and Probability Extended Nearest Neighbor Classification for Content-Based Lecture Video Retrieval
Due to the ever-increasing number of digital lecture libraries and lecture video portals, the challenge of retrieving lecture videos has become a very significant and demanding task in recent years. Accordingly, the literature presents different techniques for video retrieval by considering video co...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2017-07-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2016-0041 |
| _version_ | 1818733566505254912 |
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| author | Waykar Sanjay B. Bharathi C. R. |
| author_facet | Waykar Sanjay B. Bharathi C. R. |
| author_sort | Waykar Sanjay B. |
| collection | DOAJ |
| description | Due to the ever-increasing number of digital lecture libraries and lecture video portals, the challenge of retrieving lecture videos has become a very significant and demanding task in recent years. Accordingly, the literature presents different techniques for video retrieval by considering video contents as well as signal data. Here, we propose a lecture video retrieval system using multimodal features and probability extended nearest neighbor (PENN) classification. There are two modalities utilized for feature extraction. One is textual information, which is determined from the lecture video using optical character recognition. The second modality utilized to preserve video content is local vector pattern. These two modal features are extracted, and the retrieval of videos is performed using the proposed PENN classifier, which is the extension of the extended nearest neighbor classifier, by considering the different weightages for the first-level and second-level neighbors. The performance of the proposed video retrieval is evaluated using precision, recall, and F-measure, which are computed by matching the retrieved videos and the manually classified videos. From the experimentation, we proved that the average precision of the proposed PENN+VQ is 78.3%, which is higher than that of the existing methods. |
| first_indexed | 2024-12-17T23:51:30Z |
| format | Article |
| id | doaj.art-ef96d052c1df45c98fc4a6fa9674d706 |
| institution | Directory Open Access Journal |
| issn | 0334-1860 2191-026X |
| language | English |
| last_indexed | 2024-12-17T23:51:30Z |
| publishDate | 2017-07-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-ef96d052c1df45c98fc4a6fa9674d7062022-12-21T21:28:11ZengDe GruyterJournal of Intelligent Systems0334-18602191-026X2017-07-0126358559910.1515/jisys-2016-0041Multimodal Features and Probability Extended Nearest Neighbor Classification for Content-Based Lecture Video RetrievalWaykar Sanjay B.0Bharathi C. R.1Vel-Tech Dr.RR & Dr.SR Technical University, Chennai, IndiaVel-Tech Dr.RR & Dr.SR Technical University, Chennai, IndiaDue to the ever-increasing number of digital lecture libraries and lecture video portals, the challenge of retrieving lecture videos has become a very significant and demanding task in recent years. Accordingly, the literature presents different techniques for video retrieval by considering video contents as well as signal data. Here, we propose a lecture video retrieval system using multimodal features and probability extended nearest neighbor (PENN) classification. There are two modalities utilized for feature extraction. One is textual information, which is determined from the lecture video using optical character recognition. The second modality utilized to preserve video content is local vector pattern. These two modal features are extracted, and the retrieval of videos is performed using the proposed PENN classifier, which is the extension of the extended nearest neighbor classifier, by considering the different weightages for the first-level and second-level neighbors. The performance of the proposed video retrieval is evaluated using precision, recall, and F-measure, which are computed by matching the retrieved videos and the manually classified videos. From the experimentation, we proved that the average precision of the proposed PENN+VQ is 78.3%, which is higher than that of the existing methods.https://doi.org/10.1515/jisys-2016-0041video retrievallecture videosoptical character recognition (ocr)texturelocal vector patternclassifierprecision |
| spellingShingle | Waykar Sanjay B. Bharathi C. R. Multimodal Features and Probability Extended Nearest Neighbor Classification for Content-Based Lecture Video Retrieval Journal of Intelligent Systems video retrieval lecture videos optical character recognition (ocr) texture local vector pattern classifier precision |
| title | Multimodal Features and Probability Extended Nearest Neighbor Classification for Content-Based Lecture Video Retrieval |
| title_full | Multimodal Features and Probability Extended Nearest Neighbor Classification for Content-Based Lecture Video Retrieval |
| title_fullStr | Multimodal Features and Probability Extended Nearest Neighbor Classification for Content-Based Lecture Video Retrieval |
| title_full_unstemmed | Multimodal Features and Probability Extended Nearest Neighbor Classification for Content-Based Lecture Video Retrieval |
| title_short | Multimodal Features and Probability Extended Nearest Neighbor Classification for Content-Based Lecture Video Retrieval |
| title_sort | multimodal features and probability extended nearest neighbor classification for content based lecture video retrieval |
| topic | video retrieval lecture videos optical character recognition (ocr) texture local vector pattern classifier precision |
| url | https://doi.org/10.1515/jisys-2016-0041 |
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