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  1. Proceedings of the 1st ACM international workshop on Multimedia databases (MMDB '03)
  2. Mining spatio-temporal patterns and knowledge structures in multimedia collection
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The CPR model for summarizing video
Facilitate knowledge communications
Content based sub-image retrieval via hierarchical tree matching
Mining spatio-temporal patterns and knowledge structures in multimedia collection
Automatic generation of MPEG-7 compliant XML document for motion trajectory descriptor in sports video
Scheduling methods for broadcasting multiple continuous media data
Robust content-based image searches for copyright protection
Constructing a bowling information system with video content analysis
Kernel VA-files for relevance feedback retrieva
Image database retrieval utilizing affinity relationships
Video query processing in the VDBMS testbed for video database research
Bitmap indexing method for complex similarity queries with relevance feedback
Improving image retrieval effectiveness via multiple queries

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Mining spatio-temporal patterns and knowledge structures in multimedia collection

Content Provider ACM Digital Library
Author Chang, Shih-Fu
Abstract Detection and recognition of semantic events has been a major research challenge for multimedia indexing. An emerging direction in this field has been unsupervised discovery (mining) of patterns in spatial-temporal multimedia data. Patterns are recurrent, predictable occurrences of one or more entities that satisfy associative, statistical, or relational conditions. Patterns at the feature level may signify the occurrence of events (e.g., passing pedestrians). At the event level, patterns may represent multi-event transitions, e.g., play-break alternations in sports. Patterns in an annotated image collection may indicate collocations of related semantic concepts and perceptual clusters.Mining of patterns of different types at different levels offers rich benefits, including automatic discovery of salient events in a new domain, automatic alert generation from massive real-time data (such as surveillance data in a new environment), and discovery of novel event relationships.Many challenging issues emerge. What are the adequate representations and statistical models for patterns that may exist at different levels and different time scales? How to handle patterns that may have relatively sparse occurring frequencies? How do we evaluate the accuracy and quality of mining results given its unsupervised nature?In this talk, we will present results of our preliminary attempts in mining patterns in structured video sequences (such as sports and surveillance video) and large annotated image collections. Specifically, we will discuss the potential of statistical models like Hierarchical HMM for video mining, and the integrative exploration of electronic knowledge (such as WordNet) and statistical clustering for image knowledge mining.
Starting Page 1
Ending Page 1
Page Count 1
File Format PDF
ISBN 1581137265
DOI 10.1145/951676.951677
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2003-11-07
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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