By Masao Sakauchi (auth.), Hiroshi Arisawa, Tiziana Catarci (eds.)
Video segmentation is the main basic strategy for acceptable index ing and retrieval of video durations. as a rule, video streams are composed 1 of photographs delimited through actual shot barriers. monstrous paintings has been performed on the best way to observe such shot barriers instantly (Arman et aI. , 1993) (Zhang et aI. , 1993) (Zhang et aI. , 1995) (Kobla et aI. , 1997). throughout the inte gration of applied sciences reminiscent of photograph processing, speech/character acceptance and usual language knowing, keyword phrases might be extracted and linked to those photographs for indexing (Wactlar et aI. , 1996). A unmarried shot, although, infrequently incorporates sufficient quantity of knowledge to be significant on its own. Usu best friend, it's a semantically significant period that almost all clients have an interest in re trieving. quite often, such significant durations span numerous consecutive pictures. There infrequently exists any effective and trustworthy method, both automated or guide, to spot all semantically significant periods inside a video flow. Works through (Smith and Davenport, 1992) (Oomoto and Tanaka, 1993) (Weiss et aI. , 1995) (Hjelsvold et aI. , 1996) recommend manually defining all such inter vals within the database prematurely. in spite of the fact that, even an hour lengthy video can have an indefinite variety of significant durations. additionally, video info is multi interpretative. for this reason, given a question, what's a significant period to an annotator is probably not significant to the person who concerns the question. In perform, handbook indexing of significant periods is labour in depth and inadequate.
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Extra resources for Advances in Visual Information Management: Visual Database Systems. IFIP TC2 WG2.6 Fifth Working Conference on Visual Database Systems May 10–12, 2000, Fukuoka, Japan
G. Name, Video shots); and (3) Access from IDs to values and reverse access from values to ID. Figure 5 summarises the content-based representation of the video semantic used by MEVISE. FaceMinPosition and FaceMaxPosition are used to locate /. •..... jt" FaceMinPosition Name (125,69) (178,75) Bishop Clinton Index Management Charles ·Chirac ~"'Frame Metadata 12 456 67 Figure 5 EBG support for Video Abstraction faces inside a specific frame. The Name-FrameId relationship and videoShots-FrameId relationship enable to traverse the data structure graph according to the user's interests.
However, we observed that this type of performance gain totally depended upon the number of candidate intervals that could be discarded at the initial stage. Further experiments are necessary to establish a ground theory for benchmarking our query mechanism . 26 5. VISUAL DATABASE SYSTEMS RELATED WORK There is now growing interest in querying the large resources of digital video data. Allen's work on temporal intervals (Allen, 1983) laid the foundation for many researches concerned with time intervals (Little and Ghafoor, 1993) (Lorentzos and Mitsopoulos, 1997).
D. in Intelligence Science in 1999 from Kobe University, Japan. Since 1999 May, he is a lecturer of the Department of Computer Science and Mathematics at Kurashiki University of Science and the Arts, Japan. He was a Colombo Plan scholar during 1984-1988 and a Mombusho scholar during 19951997. A JSPS (Japan Society for the Promotion of Science) Research Fellow during the period between 1997 and 1999, his research interests include video databases, multimedia authoring, prototype-based languages and semi-structured databases.