1,720,973 research outputs found
Object-Oriented Retrieval Mechanism for Semistructured Image Collections
No full textProceedings ACM SIGMOD International Conference on Management of Data, June 2-4, 1998, Seattle, Washington, USA.Thii research was supported by the grant for the Korea
Science and En=tieering Foundation with grant number
KOSEF 95-0100-23-04-3
An indexing and retrieval mechanism for complex similarity queries in image databases
No full textA content-based image retrieval mechanism to support complex similarity queries is presented. The image content is defined by three kinds of Features: quantifiable features describing the visual information, nonquantifiable features describing the semantic information, and keywords describing more abstract semantic information. In correspondence with these feature sets, we construct three types of indexes: visual indexes, semantic indexes, and keyword indexes. Index structures are elaborated to provide effective and efficient retrieval of images based on their contents. The underlying index structure used for all indexes is the HG-tree. In addition to the HG-tree, the signature file and hashing technique are also employed to index keywords and semantic features. The proposed indexing scheme combines and extends the HG-tree, the signature file, and the hashing scheme to support complex similarity queries. We also propose anew evaluation strategy to process the complex similarity queries. Experiments have been carried out on large image collections to demonstrate the effectiveness of the proposed retrieval mechanism. (C) 1999 Academic Press
A new indexing scheme for content-based image retrieval
No full textWe propose a new efficient indexing scheme, called the Ha-tree, to support content-based retrieval in image databases. Image content is represented by a point in a multidimensional feature space. The types of queries considered are the range query and the nearest-neighbor query, both in a multidimensional space. Our goals are twofold: increasing the storage utilization and decreasing the area covered by the directory regions of the index tree. The high storage utilization and the small directory area reduce the number of nodes that have to be touched during the query processing. The first goal is achieved by suppressing node splitting if possible, and when splitting is necessary, converting two nodes into three. This is done by proposing a good ordering on the directory nodes. The second goal is achieved by maintaining the area occupied by the directory region as small as possible. This is done by introducing the smallest interval that encloses all regions of the lower nodes. We note that there is a trade-off between the two design goals, but the Ha-tree is so flexible that it can control the trade-off to some extent. We present the design of our indexing scheme and associated algorithms. In addition, we report the results of a series of tests, comparing the proposed index tree with the buddy-tree, which is one of the most successful point indexing schemes for a multidimensional space. The results show the superiority of our method
HG-tree: An index structure for multimedia databases
No full textProceedings of the Third IEEE International Conference on,06/17/1996 - 06/23/1996,Hiroshima, JapanThis research was supported in part by the Korea
Science and Engineering Foundation (KOSEF) grant and
in part by the Samsung Advanced Institute of Technology
(SAIT) grant
CONTENT-BASED LECTURE ACCESS FOR DISTANCE LEARNING
No full textIEEE International Conference on Multimedia and Expo Tokyo, Japan, Aug. 2001.This work was supported by Grant No. 2000-1-51200-007-3
from the Basic Research Program of the Korea Science and
Engineering Foundation
Multi-Mode Indices for Effective Image Retrieval in Multimedia Systems
Full text link1998 IEEE International Conference on Multimedia Computing and Systems (ICMCS'98
Multi-way Spatial Joins Using R-Trees: Methodology and Performance Evaluation
No full textAdvances in Spatial Databases, 6th International Symposium, SSD'99, Hong Kong, China, July 20-23, 199
A model for k-nearest neighbor query processing cost in multidimensional data space
No full textA cost model for the performance of the k-nearest neighbor query in multidimensional data space is presented. Two concepts, the regional average volume and the density function, are introduced to predict the performance for uniform and non-uniform data distributions. The experiment shows that the prediction based on this model is accurate within an acceptable range of the error in low and mid dimensions. (C) 1999 Elsevier Science B.V. All rights reserved
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