한국정보과학회논문지:컴퓨팅의 실제 및 레터 (Journal of KIISE:Computing Practices and Letters)

한국정보과학회 (Korean Institute of Information Scientists and Engineers)
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확장된 개념 기반 이미지 검색 시스템

An Extended Concept-based Image Retrieval System : E-COIRS

  • 발행 : 2002.06.01
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초록

In this paper, we design and implement E-COIRS enabling users to query with concepts and image features used for further refining the concepts. For example, E-COIRS supports the query "retrieve images containing black home appliance to north of reception set. "The query includes two types of concepts: IS-A and composite. "home appliance"is an IS-A concept, and "reception set" is a composite concept. For evaluating such a query. E-COIRS includes three important components: a visual image indexer, thesauri and a query processor. Each pair of objects in an mage captured by the visual image indexer is converted into a triple. The triple consists of the two object identifiers (oids) and their spatial relationship. All the features of an object is referenced by its old. A composite concept is detected by the triple thesaurus and IS-A concept is recolonized by the fuzzy term thesaurus. The query processor obtains an image set by matching each triple in a user with an inverted file and CS-Tree. To support efficient storage use and fast retrieval on high-dimensional feature vectors, E-COIRS uses Cell-based Signature tree(CS-Tree). E-COIRS is a more advanced content-based image retrieval system than other systems which support only concepts or image features.

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참고문헌

  1. A.W.M. Smeulders, 'Content-Based Image Retrieval at the End of the Early Years,' IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol. 22, No. 12, pp. 1349-1379, 2000 https://doi.org/10.1109/34.895972
  2. G. Baxter and D. Anderson, 'Image Indexing and Retrieval: Some Problems and Proposed Solutions,' New Library World, Vol. 96, No. 1123, pp. 4-13, 1995
  3. A. Pentland, R. W. Picard, S. Sclaroff, 'Photobook: Tools for Content-Based Manipulation of Image Databases,' International Journal of Computer Vision, 18(3), 1996 https://doi.org/10.1007/BF00123143
  4. M. Fickner, H. Sawhney, W. Niblack and et al., ' Query by Image and Video Content: The QBIC System,' IEEE Computer, September, 1995
  5. W. Y. Ma, 'NETRA: A Toolbox for Navigating Large Image Databases,' Ph.D. Dissertation, Dept. of Electronical and Computer Engineering, University of California at Santa Barbara, June 1997
  6. J. D. Yang and H. J. Yang, 'A Formal Framework for Image Indexing with Triples: Toward a Concept-Based Image Retrieval,' International Journal of Intelligent Systems, Vol. 14, Issue 6, 1998
  7. Wen-Syan Li, et al., 'Hierarchical image modeling for object-based media retrieval,' Data & Knowledge Engineering, Vol. 27, pp. 138-176, 1998 https://doi.org/10.1016/S0169-023X(97)00058-X
  8. S. K. Chang, Q. Y. Shi and C. W. Yan, 'Iconic indexing by 2D string,' IEEE Transaction, Pattern Analysis, pp. 413-428, 1987
  9. C. C. Chang and S. Y. Lee, 'Retrieval of Similar Pictures on Pictorical Databases,' Pattern Recognition, pp. 675-680, 1991 https://doi.org/10.1016/0031-3203(91)90034-3
  10. D. Papadias and T. Sellis, 'Spatial Reasoning Using Symbolic Arrays,' Proceeding of Int. Conference GIS-From Space to Territory Theories and Method of Spatio-Temporal Reasoning in Geographic Space, Pisa, Italy, 1992
  11. J. Wang, W. J. Yang and R. Acharya, 'Color Space Quantization for Color-Content-Based Query Systems,' Multimedia Tools and Applications, 13, pp. 73-91, 2001 https://doi.org/10.1023/A:1009629307767
  12. Markus Stricker and Markus Orengo, 'Similarity of Color Images,' Proc. SPIE Storage and Retrieving for Image and Video Databases, 1995
  13. J. Huang, 'Color-Spatial Image Indexing and Application,' Ph.D. Dissertation, Dept. of Computer Science, Cornell University, August, 1998
  14. White. D.A. and Jain. R., 'Similarity Indexing with the SS-tree,' In Proc. 12th Intl. Conf. On Data Engineering, New Orleans, pp.516-523, 1996 https://doi.org/10.1109/ICDE.1996.492202
  15. Katayama N., Satoh S., 'The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries,' Proc. ACM SIGMOD Int. Conf. on Management of Data, pp. 369-380, 1997 https://doi.org/10.1145/253262.253347
  16. K.I. Lin, H. Jagadish, C. Faloutsos, 'The TV-tree: An Index Structure for High Dimensional Data,' VLDB Journal, Vol. 3, pp. 517-542, 1994 https://doi.org/10.1007/BF01231606
  17. Berchtold S., Keim D., Kriegel H.-P., 'The X-tree: An Index Structure for High-Dimensional Data,' 22nd Conf. on Very Large Databases, 1996, Bombay, India
  18. J.D. Yang, 'An Image Retrieval Model Based on Fuzzy Triples,' Fuzzy Sets and Systems, Vol. 121, pp. 459-470, 2001 https://doi.org/10.1016/S0165-0114(00)00056-7
  19. K. T. Song, J. W. Jang, 'CS-Tree : Cell-based Signature Index Structure for Similarity Search in High-Dimensional Data,' Proceedings on KISS, 127(1), pp. 134-136, 2000