R. L. Lagendijk, A. Hanjalic, M. Ceccarelli, M. Soletic, and E. Persoon, "Visual search in a SMASH system," in Proc. IEEE Int. Conf. Image Processing, 1997, pp. 671--674.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....of video; 2) video classification; and 3) extraction of semantics. The work in the first category has concentrated on: 1) shot boundary detection and key frame extraction, e.g. 1] 34] 2) shot clustering, e.g. 32] 3) table of content creation, e.g. 9] 4) video summarization, e.g. [22], 35] and 5) video skimming [28] Manuscript received December 15, 1998; revised December 28, 1999. This paper was recommended by Associate Editor S. F. Chang. N. Haering is with Diamondback Vision, Inc. Reston, VA 20191 USA (e mail: niels dbvision.net) R. J. Qian is with Intel Labs, ....

R. L. Lagendijk, A. Hanjalic, M. Ceccarelli, M. Soletic, and E. Persoon, "Visual search in a SMASH system," in Proc. IEEE Int. Conf. Image Processing, 1997, pp. 671--674.

....frame to frame differences. In the next step, each shot is represented by a number of key frames. In our tests we used only two key frames per shot, although we believe that the performance of the algorithm can be improved using more sophisticated key frame extraction methods, as proposed in [4] [5] or [6] For each shot k, the GSU boundary likelihood L k is computed, by measuring its interrelationship to nextfollowing shots. L k represents the likelihood that the boundary between two consecutive GSUs can be assumed to lie around the shot k. In order to obtain a realistic picture about ....

Lagendijk, R.L., Hanjalic, A., Ceccarelli, M.P., Soletic, M., Persoon, E.:"Visual Search in a SMASH System", In the Proceedings of IEEE International Conference on Image Processing 1996

....of events. We show how the framework can be used to detect landing events in videos and hunts in wildlife documentaries. 1 Introduction Existing content based video indexing and retrieval methods may be classified into the following three categories: 1) syntactic structurization of video [11, 10, 7]; 2) video content classification [6, 9] and (3) extraction of semantics [5, 2, 1] We present a general approach which may be adapted to detect semantic events in different domains. To test the effectiveness of our algorithm, we use it to detect animal hunt events in wildlife documentaries and ....

R. Lagendijk, et al., "Visual Search in a SMASH System", Proc. ICIP, pp. 671-674, 1997.

....of video; 2) video classification; and (3) extraction of semantics. The work in the first category has concentrated on (a) shot boundary detection and key frame extraction, e.g. 1] 33] b) shot clustering, e.g. 31] c) table of content creation, e.g. 9] d) video summarization, e.g. [20]; and (e) video skimming [26] These methods are in general computationally simple and their performance is relatively robust. Their results, however, may not necessarily be semantically meaningful or relevant since they do not attempt to model and estimate the November 13, 1998 DRAFT 3 semantic ....

R. L. Lagendijk, A. Hanjalic, M. Ceccarelli, M. Soletic, and E. Persoon, "Visual Search in a SMASH System", Proc. International Conference on Image Processing, pp. 671-674, 1997.

....then used by a domain specific inference process at the third level to detect the video segments that contain hunts. 1. Introduction Existing content based video indexing and retrieval methods may be classified into the following three categories: 1) syntactic structurization of video, e.g. [11, 10, 7]; 2) video content classification, e.g. 6, 9] and (3) extraction of semantics, e.g. 5, 2, 1] We present a general approach which may be adapted to detect semantic events in different domains. To test the effectiveness of our algorithm, we have applied it to detect animal hunt events in ....

R. Lagendijk, et al., "Visual Search in a SMASH System", Proc. ICIP, pp. 671-674, 1997.

.... 15] It is generally accepted that content analysis of video sequences requires a preprocessing procedure that first breaks up the sequences into temporally homogeneous segments called video shots [1, 2, 3, 8, 16] then condenses these segments into one or a few representative frames (key frames) [4, 7, 21, 24], and finally determines the relationship among shots using their key frame based representation. This last step we call video content organization. Since video streams to be dealt with in modern digital storage systems are mainly in MPEG compressed form, no content related operations are possible ....

....we define our own dissimilarity measure. We assume that the video sequence is segmented into shots, using any of the methods found in literature [1, 2, 3, 8, 16] Each detected video shot is represented by one or multiple key frames such that its visual information is captured as good as possible [4, 7, 21, 24]. For dynamic shots more key frames are needed than for stationary shots. Since we are using MPEG compressed video sequences, the key frames are DC images which are typically 90 x 72 pixels, i.e. 64 times smaller the original frames. 10For each shot, all key frames are merged together in one ....

Lagendijk R.L., Hanjalic A., Ceccarelli M.P., Soletic M., Persoon E.:"Visual Search in a SMASH System", Proceedings of IEEE ICIP 1996.

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Lagendijk, R. L., A. Janjalic, M. Ceccarelli, M. Soletic, and E. Persoon, "Visual Search In a SMASH System," ICIP '96, Lausanne, CH (1996)

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