I-FGM as a real time information retrieval tool for e-governance

International Journal of Electronic Government Research

Volumn 4, Issue 1, 2008, Pages 14-35

  Santos Jr , Eugene ^a   Santos, Eunice E ^b   Nguyen, Hien ^c   Pan, Long ^b   Korah, John ^b   Xia, Huadong ^b  

^a DARTMOUTH COLLEGE   (United States)

^b VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^c UNIVERSITY OF WISCONSIN WHITEWATER   (United States)

Author keywords

Agent technology; CBIR; Distributed systems; Heterogeneous databases; Image retrieval; Information search and retrieval; Intelligent agents; IS performance evaluation; Real time IS

Indexed keywords

DISASTER PREVENTION; DISTRIBUTED DATABASE SYSTEMS; E-GOVERNMENT; IMAGE PROCESSING; IMAGE RETRIEVAL; INTELLIGENT AGENTS; NETWORK SECURITY; REAL TIME SYSTEMS;

AGENT TECHNOLOGY; CBIR; DISTRIBUTED SYSTEMS; HETEROGENEOUS DATABASE; INFORMATION SEARCH AND RETRIEVAL; IS PERFORMANCE EVALUATION; REAL TIME;

SEARCH ENGINES;

EID: 47649084171     PISSN: 15483886     EISSN: 15483894     Source Type: Journal    
DOI: 10.4018/jegr.2008010102     Document Type: Article

References (32)

1. Caenen, G., & Pauwels, E. J. (2002). Logistic regression models for relevance feedback in content-based image retrieval. San Jose, CA: Storage and Retrieval for Media Databases.
2. Carson, C., Belongie, S., Greenspan, H., & Malik, J. (2002). Blobworld: Image segmentation using expectation-maximization and its application to image querying. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(8), 1026-1038.
3. Daubechies, I. (1992). Ten lectures on wavelets. Philadelphia: SIAM.
4. Enser, P., & Sandom, C. (2003). Towards a comprehensive survey of the semantic gap in visual image retrieval. (LNCS 2728, pp. 279-287).
5. Hurd, J. D., Civco, D. L., Gilmore, M. S., Prisloe, S., & Wilson, E. H. (2006, May 1-5). Tidal wetland classification from landsal imagery using an integrated pixel-based and object-based classification approach. Paper presented at the ASPRS 2006 Annual Conference, Reno, NV.
6. Kherfi, M. L., Ziou, D., & Bernardi, A. (2004). Image retrieval from the World Wide Web: Issues, techniques, and systems. ACM Computing Surveys, 36(1), 35-67.
7. Komarek, P. (2004, May). Logistic regression for data mining and high-dimensional classification (Tech. Rep. No. CNW-RI-TR-04-34). Pittsburgh, PA: Carnegie Mellon University, Robotics Institute.
8. Ksantini, R., Ziou, D., Colin, B., & Dubeau, F. (2007). Logistic regression models for a fast CBIR method based on feature selection. Paper presented at the International Joint Conference on Artificial Intelligence.
9. Li, J., & Wang, J. Z. (2006). Real-time computerized annotation of pictures. Paper presented at the ACM Multimedia Conference, Santa Barbara, CA.
10. Li, J., Wang, J. Z., & Wiederhold, G. (2000). IRM: Integrated region matching for image retrieval. Paper presented at the ACM Multimedia Conference, Los Angeles, CA.
11. Lim, T. S., Loh, W. Y. & Shih, Y. S. (2000). A comparison of prediction accuracy, complexity, and training time for thirty-three old and new classification algorithms. Machine Learning, 40, 203-228.
12. Ma, W. Y., & Manjunath, B. (1997). NaTra: A toolbox for navigating large image databases. Paper presented at the IEEE International Conference on Image Processing.
13. Mitchell, T. (2005). Generative and discriminative classifiers: Naïve Bayes and logistic regression. Retrieved from http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf
14. Natsev, A. (2001). Multimedia retrieval by regions, concepts, and constraints. Unpublished PhD thesis, Duke University, Durham, NC.
15. Natsev, A., Chadha, A., Soetarman, B., & Vitter, J. S. (2001). CAMEL: Concept annotated iMag Elibraries. Paper presented at the Storage and Retrieval for Image and Video Databases, SPIE, San Jose, CA.
16. Natsev, A., Rastogi, R., & Shim, K. (2004). WALRUS: A similarity retrieval algorithm for image databases. IEEE Transactions on Knowledge and Data Engineering, 16(3), 301-316.
17. Perlich, C., Provost, F., & Simonoff, J. (2003). Tree induction vs. logistic regression: A learning-curve analysis. Journal of Machine Learning Research, 4, 211-255.
18. Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Francisco: Morgan Kaufmann.
19. Rui, Y., Huang, T., & Chang, S. (1999). Image retrieval: Current techniques, promising directions and open issues. Journal of Visual Communication and Image Representation, 10(4), 39-62.
20. Salton, G., & McGill, M. (1983). Introduction to modern information retrieval. McGraw-Hill.
21. Santos, E., Jr., Santos, E., Nguyen, H., Pan, L., & Korah, J. (2005). Large-scale distributed foraging, gathering, and matching for information retrieval: Assisting the geospatial intelligent analyst. In Proceedings of the SPIE (Vol. 5803, pp. 66-77).
22. Santos, E., Jr., Santos, E., Nguyen, H., Pan, L., Korah, J., Zhao, Q., et al. I-FGM information retrieval in highly dynamic search spaces. In Proceedings of SPIE (Vol. 6229, pp. 1-12).
23. Santos, E., Jr., Santos, E., Nguyen, H., Pan, L., Korah, J., Zhao, Q., et al. (2007). Applying I-FGM to image retrieval and an I-FGM system performance analyses. In Proceedings of the SPIE: Defense \& Security Symposium (Vol. 6560), Orlando, FL.
24. Smeulders, A. W. M., Worring, M., Santini, S., Gupta, A., & Jain, R. (2000). Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), 1349-1380.
25. Tsymbalenko, Y., & Munson, E. V. (2001). Using HTMI, metadata to find relevant images on the Web. Paper presented at the Internet Computing, Las Vegas, NV.
26. Vapnik, V. (1999). The nature of statistical learning theory (2nd ed.). Springer Verlag.
27. Veltkamp, R. C., & Tanase, M. (2000). Content-based image retrieval systems: A survey (Tech. Rep. No. UU-CS-2000-34). The Netherlands: Utrecht University, Department of Computer Science.
28. Wang, J. Z., Li, J., & Wiederhold, G. (2001). SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9), 947-963.
29. Witten, I. H., & Frank, E. (2005, June). Data mining: Practical machine learning tools and techniques (2nd ed.). Morgan Kaufmann.
30. Zhang, J., Jin, R., Yang, Y., & Hauptmann, A. G. (2003). Modified logistic regression: An approximation to SVM and its application in large-scale text categorization. In Proceedings of the Twentieth International Conference on Machine Learning 2003, Washington DC.
31. Zhang, T., Ramakrishnan, R., & Livny, M. (1996). BIRCH: An efficient data clustering method for very large databases. Paper presented at the ACM SIGMOD International Conference on Managment of Data, Montreal, Canada.
32. Zhu, X., Sun, S., Cheng, S. E., & Bern, M. (2004). Classification of protein crystallization imagery. Paper presented at the 26th Annual International Conference of IEEE Engineering in Medicine and Biology Society, San Francisco.