Content-based image retrieval using image regions as query examples

Conferences in Research and Practice in Information Technology Series

Volumn 75, Issue , 2008, Pages 39-47

  Awang Iskandar, D N F ^a   Thom, James A ^a   Tahaghoghi, S M M ^a  

^a RMIT UNIVERSITY   (Australia)

Author keywords

Cbir; Linear combination; Machine learning; Query by example

Indexed keywords

CBIR; CONTENT BASED IMAGE RETRIEVAL; IMAGE REGIONS; LINEAR COMBINATIONS; LOW-LEVEL FEATURES; MULTIPLE REGIONS; QUERY-BY-EXAMPLE; RETRIEVAL EFFECTIVENESS; SHAPE FEATURES;

CONTENT BASED RETRIEVAL; LEARNING ALGORITHMS; LEARNING SYSTEMS;

QUERY PROCESSING;

EID: 84873313082     PISSN: 14451336     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. Awang Iskandar, D. N. F., Thom, J. A. & Tahaghoghi, S. M. M. (2006), Querying comics using multiple shape examples, Technical Report TR-06-5, Royal Melbourne Institute of Technology University
2. Belongie, S., Malik, J. & Puzicha, J. (2000), Shape context: A new descriptor for shape matching and object recognition, in 'Neural Information Processing Systems', pp. 831-837
3. Breiman, L. (2001), 'Random forests', Machine Learning 45(1), 5-32
4. Burges, C. J. C. (1998), 'A tutorial on support vector machines for pattern recognition', Data Mining and Knowledge Discovery 2(2), 121-167
5. Carson, C., Thomas, M., Belongie, S., Hellerstein, J. M. & Malik, J. (1999), Blobworld: A system for region-based image indexing and retrieval, in 'Proceedings of the Third International Conference on Visual Information Systems', Springer
6. Cooper, G. F. & Herskovits, E. (1992), 'A Bayesian method for the induction of probabilistic networks from data', Machine Learning 9(4), 309-347
7. Costa, L. & Cesar Jr., R. M. (2000), Shape Analysis and Classiffcation: Theory and Practice, CRC Press, Inc., Boca Raton, FL, USA
8. Dougherty, J., Kohavi, R. & Sahami, M. (1995), Supervised and unsupervised discretization of continuous features, in 'Proceedings of the International Conference on Machine Learning', pp. 194-202
9. Enser, P. (1993), 'Query analysis in a visual information retrieval context', Journal of Document and Text Management 1(1), 25-52
10. Fawcett, T. & Mishra, N., eds (2003), Tackling the Poor Assumptions of Naïve Bayes Text Classifiers, AAAI Press
11. Gevers, T. & Smeulders, A. W. M. (2000), 'Pic-ToSeek: Combining Color and Shape Invariant Features for Image Retrieval', IEEE transactions Image Processing 9-1, 102-119
12. Hare, J. S., Lewis, P. H., Enser, P. G. B. & Sandom, C. J. (2006), Mind the gap: Another look at the problem of the semantic gap in image retrieval, in Chang, E. Y., Hanjalic, A. and Sebe, N., ed., 'Proceedings of Multimedia Content Analysis, Management and Retrieval 2006 SPIE', Vol. 6073, pp. 607309-1
13. Iqbal, Q. & Aggarwal, J. K. (2002), Cires: A system for content-based retrieval in digital image libraries, in 'Proceedings of International Conference on Control, Automation, Robotics and Vision (ICARCV)', pp. 205-210
14. Jain, A. & Vailaya, A. (1996), 'Image Retrieval using Color and Shape', Pattern Recognition Society 29(8), 1233-1244
15. John, G. H. & Langley, P. (1995), Estimating continuous distributions in Bayesian classifiers, in 'Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence', Morgan Kaufmann, San Mateo, pp. 338-345
16. Kohavi, R. (1996), Scaling up the accuracy of Naïve-Bayes classifiers: a decision-tree hybrid, in 'Proceedings of the Second International Conference on Knowledge Discovery and Data Mining', pp. 202-207
17. Landwehr, N., Hall, M. & Frank, E. (2005), 'Logistic model trees', Machine Learning 59(1-2), 161-205
18. Latecki, L., Melter, R. & Gross, A. (2002), 'Special issue: Shape Representation and Similarity for Image Databases', Pattern Recognition 35(1), 1-2
19. Lavrac, N., Gamberger, D., Todorovski, L. & Blockeel, H., eds (2003), Logistic Model Trees, Vol. 2837 of Lecture Notes in Computer Science, Springer
20. Lew, M. S., Sebe, N., Djeraba, C. & Jain, R. (2006), 'Content-based multimedia information retrieval: State of the art and challenges', ACM Transaction on Multimedia Computing, Communication and Applications 2(1), 1-19
21. Lewis, D. D. (1998), Naïve (Bayes) at forty: The independence assumption in information retrieval., in C. Nédellec & C. Rouveirol, eds, 'Proceedings of ECML-98, 10th European Conference on Machine Learning', number 1398, Springer Verlag, Heidelberg, DE, Chemnitz, DE, pp. 4-15
22. Quinlan, J. R. (1993), C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA
23. Remco, R. B. (2004), 'Bayesian network classifiers in weka'. (Working paper series. University of Waikato, Department of Computer Science. No.14/2004). Hamilton, New Zealand: University of Waikato.URL: http://researchcommons.waikato.ac.nz/cms_papers/36
24. Smeulders, A., 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. Squire, D. M., Müller, W., Müller, H. & Pun, T. (2000), 'Content-based Query of Image Databases: Inspirations from Text Retrieval', Pattern Recognition Letters 21(13-14), 1193-1198. (special edition for SCIA'99)
26. Tahaghoghi, S. M. M., Thom, J. A. & Williams, H. E. (2001), Are two pictures better than one?, in 'Proceedings of the 12th Australasian Database Conference (ADC'2001)', Vol. 23, Gold Coast, Australia, pp. 138-144
27. Tahaghoghi, S. M. M., Thom, J. A. & Williams, H. E. (2002), 'Multiple-example queries in content-based image retrieval', Proceedings of the Ninth International Symposium on String Processing and Information Retrieval (SPIRE'2002) pp. 227-240
28. Vasconcelos, N. & Kunt, M. (2001), Content-based retrieval from image databases: Current solutions and future directions, in 'Proceedings of International Conference on Image Processing', pp. 6-9
29. Witten, I. H. & Frank, E. (2005), Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Series in Data Management Systems, second edn, Morgan Kaufmann
30. Witten, I. H., Frank, E., Trigg, L., Hall, M., Holmes, G. & Cunningham, S. J. (1999), Weka: Practical machine learning tools and techniques with Java implementations, in N. Kasabov & K. Ko, eds, 'Proceedings of the ICONIP/ANZIIS/ANNES'99 Workshop on Emerging Knowledge Engineering and Connectionist-Based Information Systems', pp. 192-196. Dunedin, New Zealand
31. Zhang, D. & Lu, G. (2004), 'Review of Shape Representation and Description Techniques', Pattern Recognition Society 37, 1-19