B.Gunsel, A.Ferman, etc, "Temporal Video Segmentation Using Unsupervised Clustering and Semantic Object Tracking", Journal of Electronic Imaging 1998, pp. 592604 Home/Search Document Details and Download
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Automated Video Segmentation - Ren, Sharma, Singh (2001) (Correct)
....which one gives the lowest index value. It should be noted here that our approach is not the only logical method of finding scene changes by clustering. It is also possible to cluster the results of the similarity metrics as a two class clustering problem: scene change and no scene change [3]. This will identify peaks that represent scene changes and no scene changes. 3. Experimental Details In our experiments we have chosen a bicycle video sequence. This sequence has a total of 236 frames. The genuine scene changes occur at frames 31 and 63. This is shown in Figure 2. In order to ....
B. Gnsel, A.M. Ferman and A.M. Tekalp, "Temporal video segmentation using unsupervised clustering and semantic object tracking", Electronic Imaging, vol. 7, no. 3, pp. 592-604, 1998.
Content-based Video Parsing and Indexing based on.. - Tsekeridou, Pitas (2001) (5 citations) (Correct)
....indexing methods provide tools for queries based on high level semantics without providing examples to the system. Another approach to video parsing and indexing attempts to provide visual or iconic summaries to the user organized in a hierarchical manner in order to facilitate browsing [30] [37]. Compact visual representations are created to serve this purpose such as: micons, video posters, mosaic representations. A third approach to video indexing aims at serving user queries by example [2] 31] 38] 39] A visual query is submitted by the user and similarity measures are de ned to ....
B. Guensel, A.M. Ferman, and M.A. Tekalp, \Temporal video segmentation using unsupervised clustering and semantic object tracking", SPIE Journal of Electronic Imaging, vol. 7, no. 3, pp. 592-604, 1998.
Temporal Video Segmentation: A Survey - Koprinska, Carrato (2001) (2 citations) (Correct)
....on very dark video images. 2.1.4 Clustering Based Temporal Video Segmentation The approaches discussed so far rely on suitable thresholding of similarities between successive frames. However, the thresholds are typically highly sensitive to the type of input video. This drawback is overcome in [13] by the application of unsupervised clustering algorithm. More specifically, the temporal video segmentation is viewed as a 2 class clustering problem ( scene change and no scene change ) and the well known K means algorithm [27] is used to cluster frame dissimilarities. Then the frames from the ....
B. Gnsel, A. M. Ferman, A. M. Tekalp, Temporal video segmentation using unsupervised clustering and semantic object tracking, Journal of Electronic Imaging, 7(3), (1998) 592-604.
Model-Based Classification of Visual Information for.. - Jaimes, Chang (1999) (5 citations) (Correct)
.... [22] 24] Our framework for classification of visual information differs from other approaches based on regions [21] 15] 7] that perform classification based on global region configuration, and from others [18] that neither use spatial relationships nor allow definition of complex objects [3]. Our approach is different from [10] in the following: 1. users construct their own models (rather than having a fixed set) 2. No assumption is made about elements of the model (regions have arbitrary shapes, cylinder like primitives not assumed) 3. Generic region extraction rather than only ....
B. Gunsel, A.M. Ferman, and A.M. Tekalp, "Temporal video segmentation using unsupervised clustering and semantic object tracking", Journal of Electronic Imaging 7(3), 592-604, July 1998 SPIE IS&T.
Supervised Classification For Video Shot Segmentation - Yanjun Qi Alexander (2003) (Correct)
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B.Gunsel, A.Ferman, etc, "Temporal Video Segmentation Using Unsupervised Clustering and Semantic Object Tracking", Journal of Electronic Imaging 1998, pp. 592604
Video Transition: Modelling and Prediction - Ren, Singh (Correct)
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B. Gunsel, A.M. Ferman and A.M. Tekalp, "Temporal video segmentation using unsupervised clustering and semantic object tracking", SPIE Journal of Electronic Imaging, vol.7, no.3, pp.592-604, July 1998.
Automated Video Segmentation - Ren, Sharma, Singh (2001) (Correct)
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B. Gnsel, A.M. Ferman and A.M. Tekalp, Temporal video segmentation using unsupervised clustering and semantic object tracking, Electronic Imaging, vol. 7, no. 3, pp. 592-604, 1998.
MultiView: Multilevel video content representation.. - Fan, Aref.. (2001) (1 citation) (Correct)
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B. Gunsel, A. M. Ferman, and A. M. Tekalp, "Temporal video segmentation using unsupervised clustering and semantic object tracking, " J. Electron. Imaging 7, 592--604 #1998#.
Supervised Classification For Video Shot Segmentation - Yanjun Qi Alexander (2003) (Correct)
No context found.
B.Gunsel, A.Ferman, etc, "Temporal Video Segmentation Using Unsupervised Clustering and Semantic Object Tracking", Journal of Electronic Imaging 1998, pp. 592604
Unsupervised Clustering of Images using their Joint.. - Seldin, Starik, Werman (Correct)
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B. Gunsel, A. Ferman, and A. Tekalp. Temporal video segmentation using unsupervised clustering and semantic object tracking, 1998.
Model-Based Video Classification toward Hierarchical.. - Fan, Al. (2002) (1 citation) (Correct)
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B. Gunsel, A.M. Ferman, and A.M. Tekalp, "Temporal video segmentation using unsupervised clustering and semantic object tracking," J. Electronic Imaging, Vol. 7, pp. 592--604, 1998.
Learning Structured Visual Detectors From User Input At Multiple.. - Jaimes (2001) (1 citation) (Correct)
No context found.
B. Gunsel, A.M. Ferman, and A.M. Tekalp, "Temporal video segmentation using unsupervised clustering and semantic object tracking," IS&T/SPIE Journal of Electronic Imaging, 7(3):592-604, July 1998.
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