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  1. Proceedings of the 1st ACM international workshop on Multimedia databases (MMDB '03)
  2. Bitmap indexing method for complex similarity queries with relevance feedback
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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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Bitmap indexing method for complex similarity queries with relevance feedback

Content Provider ACM Digital Library
Author Cha, Guang-Ho
Abstract The similarity indexing and searching is well known to be a difficult one for high-dimensional applications such as multimedia databases. Especially, it becomes more difficult when multiple features have to be indexed together. Moreover, few indexing methods are currently available to effectively support disjunctive queries for relevance feedback.In this paper, we propose a novel indexing method that is designed to efficiently handle complex similarity queries as well as relevance feedback in high-dimensional image and video databases. In order to provide the indexing method with the flexibility in control multiple features and multiple query objects, our method treats every dimension independently. The efficiency of our method is realized by a specialized bitmap indexing that represents all objects in a database as a set of bitmaps. The percentage of data accessed in our indexing method is inversely proportional to the overall dimensionality, and thus the performance deterioration with the increasing dimensionality does not occur.Our main contributions are three-fold: (1) We provide a novel way to index high-dimensional data; (2) Our method efficiently handles complex similarity queries; and (3) Disjunctive queries driven by relevance feedback are efficiently treated. Our empirical results demonstrate that our indexing method achieves speedups of 10 to 15 over the linear scan.
Starting Page 55
Ending Page 62
Page Count 8
File Format PDF
ISBN 1581137265
DOI 10.1145/951676.951687
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2003-11-07
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Relevance feedback High-dimensional index Bitmap index Content-based image retrieval Similarity search
Content Type Text
Resource Type Article
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