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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 43, Iss. 2 — Jan. 10, 2004
  • pp: 210–217

Using texture to analyze and manage large collections of remote sensed image and video data

Shawn Newsam, Lei Wang, Sitaram Bhagavathy, and Bangalore S. Manjunath  »View Author Affiliations


Applied Optics, Vol. 43, Issue 2, pp. 210-217 (2004)
http://dx.doi.org/10.1364/AO.43.000210


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Abstract

We describe recent research into using the visual primitive of texture to analyze and manage large collections of remote sensed image and video data. Texture is regarded as the spatial dependence of pixel intensity. It is characterized by the amount of dependence at different scales and orientations, as measured with frequency-selective filters. A homogeneous texture descriptor based on the filter outputs is shown to enable (1) content-based image retrieval in large collections of satellite imagery, (2) semantic labeling and layout retrieval in an aerial video management system, and (3) statistical object modeling in geographic digital libraries.

© 2004 Optical Society of America

OCIS Codes
(100.2960) Image processing : Image analysis
(100.5010) Image processing : Pattern recognition
(110.2960) Imaging systems : Image analysis

History
Original Manuscript: May 20, 2003
Revised Manuscript: July 7, 2003
Published: January 10, 2004

Citation
Shawn Newsam, Lei Wang, Sitaram Bhagavathy, and Bangalore S. Manjunath, "Using texture to analyze and manage large collections of remote sensed image and video data," Appl. Opt. 43, 210-217 (2004)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-43-2-210


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References

  1. A. Huertas, R. Nevatia, “Detecting buildings in aerial images,” Comput. Vision Graph. Image Process. 41(2), 131–152 (1988). [CrossRef]
  2. J. Shufelt, D. M. McKeown, “Fusion of monocular cues to detect man-made structures in aerial imagery,” Comput. Vision Graph. Image Process. 57(3), 307–330 (1993).
  3. T. Chang, C.-C. J. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Process. 2(4), 429–441 (1993). [CrossRef]
  4. R. M. Haralick, K. Shanmugam, I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973). [CrossRef]
  5. F. Liu, R. W. Picard, “Periodicity, directionality, and randomness: wold features for image modeling and retrieval,” IEEE Trans. Pattern Anal. Mach. Intell. 18(7), 722–733 (1996). [CrossRef]
  6. J. Mao, A. Jain, “Texture classification and segmentation using multiresolution simultaneous autoregressive models,” Pattern Recogn. 25(2), 173–188 (1992). [CrossRef]
  7. R. Chellappa, S. Chatterjee, “Classification of textures using Gaussian Markov random fields,” IEEE Trans. Acoust. Speech Signal Process. 33, 959–963 (1985). [CrossRef]
  8. B. S. Manjunath, W. Y. Ma, “Texture features for browsing and retrieval of image data,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 837–842 (1996). [CrossRef]
  9. J. Daugman, “Complete discrete 2D Gabor transform by neural networks for image analysis and compression,” IEEE Trans. Acoust. Speech Signal Process. 36, 1169–1179 (1988). [CrossRef]
  10. S. Marcelja, “Mathematical description of the responses of simple cortical cells,” J. Opt. Soc. Am. 70, 1297–1300 (1980). [CrossRef] [PubMed]
  11. P. Wu, B. S. Manjunath, S. Newsam, H. D. Shin, “A texture descriptor for browsing and similarity retrieval,” J. Signal Process. 16(1–2), 33–43 (2000).
  12. B. S. Manjunath, P. Salembier, T. Sikora, eds., Introduction to MPEG7: Multimedia Content Description Interface (Wiley, New York, 2002).
  13. M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafine, D. Lee, D. Petkovic, D. Steele, P. Yanker, “Query by image and video content: the QBIC system,” IEEE Comput. 28(9), 23–32 (1995). [CrossRef]
  14. J. R. Bach, C. Fuller, A. Gupta, A. Hampapur, B. Horowitz, R. Humphrey, R. Jain, C. F. Shu, “The Virage image search engine: an open framework for image management,” in Storage and Retrieval for Image and Video Databases IV, I. K. Sethi, R. C. Jain, eds., Proc. SPIE2670, 76–87 (1996). [CrossRef]
  15. A. Pentland, R. W. Picard, S. Sclaroff, “Photobook: content-based manipulation of image databases,” Int. J. Comput. Vision 18(3), 233–254 (1996). [CrossRef]
  16. J. R. Smith, S.-F. Chang, “VisualSEEk: a fully automated content-based image query system,” in Proceedings of the Fourth ACM International Conference on Multimedia (AMC Multimedia, Berkeley, Calif., 1996), pp. 87–98. [CrossRef]
  17. S. Newsam, J. Tes̆ić, M. Saban, B. S. Manjunath, MPEG-7 Homogeneous Texture Descriptor Demo, http://vision.ece.ucsb.edu/texture/mpeg7/instructions.html .
  18. S. Z. Li, Markov Random Field Modeling in Image Analysis (Springer-Verlag, Berlin, 2001). [CrossRef]
  19. S. Bhagavathy, S. Newsam, B. S. Manjunath, “Modeling object classes in aerial images using texture motifs,” in Proceedings of the International Conference on Pattern Recognition (IEEE Computer Society, Los Alamitos, Calif., 2002), pp. 981–984.
  20. A. P. Dempster, N. M. Laird, D. B. Rubin, “Maximum likelihood estimation from incomplete data via the EM algorithm,” J. R. Statistical Soc. Ser. B 39(1), 1–38 (1977).
  21. The Alexandria Digital Library Project, http://www.alexandria.ucsb.edu .

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