Characterization of common videos with statistical features extracted from frame transition profiles
Abhiram Reddy Gaddampalli, Qiuming Zhu
The availability of a tremendous amount of videos on Internet nowadays raises a research question of how to automatically classify and label the videos in terms of their contents so as to allow people a quick access based on a particular interest in the types and characteristics of the videos. Organizing video clips into proper categories will make the process of content-based search on large number of videos much faster and improve the accessibility. Also an ability to detect and identify the originality and the creator (author/source) according to the unique shooting characteristics of the videos will be useful in certain applications. A profile created by extracting the features of shot transitions at which the contents of video frames exhibit special patterns of changes helps categorizing videos in different types. The research of this paper experimented on three types of videos (News, Sports, and Music) to show that a content profile built on a new set of frame transition parameters and corresponding statistical measurements could be applied to reveal the specific characteristics and distinguish the different types of videos.
2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
https://doi.org/10.1109/SSCI.2017.8285277
2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
Duration: Nov 27 2017 → Dec 1 2017
2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
11/27/17 → 12/1/17
Video classification
content profiling
shot transitions
transitional features
Gaddampalli, A. R., & Zhu, Q. (2018). Characterization of common videos with statistical features extracted from frame transition profiles. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings (pp. 1-7). (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2017.8285277
Characterization of common videos with statistical features extracted from frame transition profiles. / Gaddampalli, Abhiram Reddy; Zhu, Qiuming.
2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-7 (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings; Vol. 2018-January).
Gaddampalli, AR & Zhu, Q 2018, Characterization of common videos with statistical features extracted from frame transition profiles. in 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-7, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017, Honolulu, United States, 11/27/17. https://doi.org/10.1109/SSCI.2017.8285277
Gaddampalli AR, Zhu Q. Characterization of common videos with statistical features extracted from frame transition profiles. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-7. (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings). https://doi.org/10.1109/SSCI.2017.8285277
Gaddampalli, Abhiram Reddy ; Zhu, Qiuming. / Characterization of common videos with statistical features extracted from frame transition profiles. 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-7 (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings).
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title = "Characterization of common videos with statistical features extracted from frame transition profiles",
abstract = "The availability of a tremendous amount of videos on Internet nowadays raises a research question of how to automatically classify and label the videos in terms of their contents so as to allow people a quick access based on a particular interest in the types and characteristics of the videos. Organizing video clips into proper categories will make the process of content-based search on large number of videos much faster and improve the accessibility. Also an ability to detect and identify the originality and the creator (author/source) according to the unique shooting characteristics of the videos will be useful in certain applications. A profile created by extracting the features of shot transitions at which the contents of video frames exhibit special patterns of changes helps categorizing videos in different types. The research of this paper experimented on three types of videos (News, Sports, and Music) to show that a content profile built on a new set of frame transition parameters and corresponding statistical measurements could be applied to reveal the specific characteristics and distinguish the different types of videos.",
keywords = "Video classification, content profiling, feature extraction, shot transitions, transitional features",
author = "Gaddampalli, {Abhiram Reddy} and Qiuming Zhu",
doi = "10.1109/SSCI.2017.8285277",
series = "2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings",
booktitle = "2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings",
T1 - Characterization of common videos with statistical features extracted from frame transition profiles
AU - Gaddampalli, Abhiram Reddy
AU - Zhu, Qiuming
N2 - The availability of a tremendous amount of videos on Internet nowadays raises a research question of how to automatically classify and label the videos in terms of their contents so as to allow people a quick access based on a particular interest in the types and characteristics of the videos. Organizing video clips into proper categories will make the process of content-based search on large number of videos much faster and improve the accessibility. Also an ability to detect and identify the originality and the creator (author/source) according to the unique shooting characteristics of the videos will be useful in certain applications. A profile created by extracting the features of shot transitions at which the contents of video frames exhibit special patterns of changes helps categorizing videos in different types. The research of this paper experimented on three types of videos (News, Sports, and Music) to show that a content profile built on a new set of frame transition parameters and corresponding statistical measurements could be applied to reveal the specific characteristics and distinguish the different types of videos.
AB - The availability of a tremendous amount of videos on Internet nowadays raises a research question of how to automatically classify and label the videos in terms of their contents so as to allow people a quick access based on a particular interest in the types and characteristics of the videos. Organizing video clips into proper categories will make the process of content-based search on large number of videos much faster and improve the accessibility. Also an ability to detect and identify the originality and the creator (author/source) according to the unique shooting characteristics of the videos will be useful in certain applications. A profile created by extracting the features of shot transitions at which the contents of video frames exhibit special patterns of changes helps categorizing videos in different types. The research of this paper experimented on three types of videos (News, Sports, and Music) to show that a content profile built on a new set of frame transition parameters and corresponding statistical measurements could be applied to reveal the specific characteristics and distinguish the different types of videos.
KW - Video classification
KW - content profiling
KW - feature extraction
KW - shot transitions
KW - transitional features
U2 - 10.1109/SSCI.2017.8285277
DO - 10.1109/SSCI.2017.8285277
T3 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
10.1109/SSCI.2017.8285277