Semisupervised Segmentation of UHD Video

0494104 - ÚI 2019 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Keruľ-Kmec, O. - Pulc, Petr - Holeňa, Martin
Semisupervised Segmentation of UHD Video.
ITAT 2018: Information Technologies – Applications and Theory. Proceedings of the 18th conference ITAT 2018. Aachen: Technical University & CreateSpace Independent Publishing Platform, 2018 - (Krajči, S.), s. 100-107. CEUR Workshop Proceedings, V-2203. ISSN 1613-0073.
[ITAT 2018. Conference on Information Technologies – Applications and Theory /18./. Plejsy (SK), 21.09.2018-25.09.2018]
Grant CEP: GA ČR(CZ) GA18-18080S
Institucionální podpora: RVO:67985807
Klíčová slova: UHD video * Scene segmentation * Keypoint detector * Semisupervised classification * Cluster regularization * C-means
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2203/100.pdf

One of the key preprocessing tasks in information retrieveal from video is the segmentation of the scene, primarily its segmentation into foreground objects and the background. This is actually a classification task, but with the specific property that it is very time consuming and costly to obtain human-labelled training data for classifier training. That suggests to use semisupervised classifiers to this end. The presented work in progress reports the investigation of semisupervised classification methods based on cluster regularization and on fuzzy c-means in connection with the foreground / background segmentation task. To classify as many video frames as possible using only a single human-based frame, the semisupervised classification is combined with a frequently used keypoint detector based on a combination of a corner detection method with a visual descriptor method. The paper experimentally compares both methods, and for the first of them, also classifiers with different delays between the human-labelled video frame and classifier training.
Trvalý link: http://hdl.handle.net/11104/0287344