Video abstraction using density-based clustering algorithm
The exponential growth in the number of surveillance videos makes the search and retrieval of their contents an extensive, time-consuming, and tedious task. Video abstraction is a general solution to alleviate this problem by generating a short and concise version of the original video. The existing...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer Verlagservice@springer.de
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/74402/ http://psasir.upm.edu.my/id/eprint/74402/1/74402.pdf |
| _version_ | 1848857493408579584 |
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| author | Chamasemani, Fereshteh Falah Affendey, Lilly Suriani Mustapha, Norwati Khalid, Fatimah |
| author_facet | Chamasemani, Fereshteh Falah Affendey, Lilly Suriani Mustapha, Norwati Khalid, Fatimah |
| author_sort | Chamasemani, Fereshteh Falah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The exponential growth in the number of surveillance videos makes the search and retrieval of their contents an extensive, time-consuming, and tedious task. Video abstraction is a general solution to alleviate this problem by generating a short and concise version of the original video. The existing abstraction approaches have commonly relied on global characteristics of visual content and neglected the local details of video frames. This paper presents an enhanced video abstraction approach called Density-based Surveillance video abstraction (DbSva) to generate a static short-length video. The novelty of DbSva is (a) to integrate the advantages of both the global and local features of video contents by fusion and (b) to employ the DENsity-based CLUstEring algorithm (DENCLUE) to significantly improve the quality of abstract videos. Utilizing fusion and the DENCLUE algorithm resulted in the extraction of more informative parts of the videos and increased the robustness of the proposed approach to handle large-scale and noisy videos with no further tuning of the input parameters. A number of qualitative and quantitative experiments support the effectiveness of the proposed approach in generating higher-quality abstract videos compared to the other approaches. |
| first_indexed | 2025-11-15T11:58:26Z |
| format | Article |
| id | upm-74402 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:58:26Z |
| publishDate | 2017 |
| publisher | Springer Verlagservice@springer.de |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-744022025-06-23T07:19:47Z http://psasir.upm.edu.my/id/eprint/74402/ Video abstraction using density-based clustering algorithm Chamasemani, Fereshteh Falah Affendey, Lilly Suriani Mustapha, Norwati Khalid, Fatimah The exponential growth in the number of surveillance videos makes the search and retrieval of their contents an extensive, time-consuming, and tedious task. Video abstraction is a general solution to alleviate this problem by generating a short and concise version of the original video. The existing abstraction approaches have commonly relied on global characteristics of visual content and neglected the local details of video frames. This paper presents an enhanced video abstraction approach called Density-based Surveillance video abstraction (DbSva) to generate a static short-length video. The novelty of DbSva is (a) to integrate the advantages of both the global and local features of video contents by fusion and (b) to employ the DENsity-based CLUstEring algorithm (DENCLUE) to significantly improve the quality of abstract videos. Utilizing fusion and the DENCLUE algorithm resulted in the extraction of more informative parts of the videos and increased the robustness of the proposed approach to handle large-scale and noisy videos with no further tuning of the input parameters. A number of qualitative and quantitative experiments support the effectiveness of the proposed approach in generating higher-quality abstract videos compared to the other approaches. Springer Verlagservice@springer.de 2017-08 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/74402/1/74402.pdf Chamasemani, Fereshteh Falah and Affendey, Lilly Suriani and Mustapha, Norwati and Khalid, Fatimah (2017) Video abstraction using density-based clustering algorithm. Visual Computer, 34 (10). pp. 1299-1314. ISSN 0178-2789; eISSN: 0178-2789 https://link.springer.com/article/10.1007/s00371-017-1432-3?error=cookies_not_supported&code=00c73a53-4c58-4298-9d77-2b0370bb498f 10.1007/s00371-017-1432-3 |
| spellingShingle | Chamasemani, Fereshteh Falah Affendey, Lilly Suriani Mustapha, Norwati Khalid, Fatimah Video abstraction using density-based clustering algorithm |
| title | Video abstraction using density-based clustering algorithm |
| title_full | Video abstraction using density-based clustering algorithm |
| title_fullStr | Video abstraction using density-based clustering algorithm |
| title_full_unstemmed | Video abstraction using density-based clustering algorithm |
| title_short | Video abstraction using density-based clustering algorithm |
| title_sort | video abstraction using density-based clustering algorithm |
| url | http://psasir.upm.edu.my/id/eprint/74402/ http://psasir.upm.edu.my/id/eprint/74402/ http://psasir.upm.edu.my/id/eprint/74402/ http://psasir.upm.edu.my/id/eprint/74402/1/74402.pdf |