A Feature Vector Approach for Inter-Query Learning for Content-Based Image Retrieval

Kien-Ping Chung; Chun Che Fung

doi:10.20965/jaciii.2007.p0289

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JACIII Vol.11 No.3 pp. 289-293

doi: 10.20965/jaciii.2007.p0289

(2007)

Paper:

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A Feature Vector Approach for Inter-Query Learning for Content-Based Image Retrieval

Kien-Ping Chung and Chun Che Fung

School of Information Technology, Murdoch University, South Street, Murdoch, Perth, Australia, 6150

Received:

April 20, 2006

Accepted:

July 29, 2006

Published:

March 20, 2007

Keywords:

content-based image retrieval system, inter-query learning, relevance feedback, statistical discriminant analysis

Abstract

Use of relevance feedback (RF) in the feature vector model has been one of the most widely used approaches to fine tuning queries for content-based image retrieval (CBIR). We propose a framework that extends RF to capturing the inter-query relationship between current and previous queries. Using the feature vector model, this avoids the need to “memorize” actual retrieval relationships between actual image indexes and the previous queries. This approach is suited to image database applications in which images are frequently added and removed. In the previous work [1], we developed a feature vector framework for inter-query learning using statistical discriminant analysis. One weakness of the previous framework is that the criteria for exploring and merging with an existing visual group are based on two constant thresholds, which are selected through trial and error. Another weakness is that it is not suited to mutually interrelated data clusters. Instead of using constant values, we have further extended the framework using positive feedback sample size as a factor for determining thresholds. Experiments demonstrated that our proposed framework outperforms the previous framework.

Cite this article as:

K. Chung and C. Fung, “A Feature Vector Approach for Inter-Query Learning for Content-Based Image Retrieval,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.3, pp. 289-293, 2007.

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References

[1] K.-P. Chung, K. W. Wong, and C. C. Fung, “Reducing User Log Size in an Inter-Query Learning Content-Based Image Retrieval (CBIR) System with a Cluster Merging Approach,” International Joint Conference on Neural Networks, Vancouver, Canada, pp. 2163-2170, 2006.
[2] X. S. Zhou and T. S. Huang, “Relevance Feedback in Image Retrieval: A Comprehensive Review,” ACM Multimedia Systems Journal, Vol.8, pp. 536-544, 2003.
[3] D. R. Heisterkamp, “Building a Latent Semantic Index of an Image Database from Patterns of Relevance Feedback,” Proceedings of International Conference on Pattern Recognition, 4, pp. 134-137, 2002.
[4] M. Li, Z. Chen, and H. J. Zhang, “Statistical Correlation Model for Image Retrieval,” Pattern Recognition, Vol.35, pp. 2687-2693, 2002.
[5] J. Han, K. N. Ngan, M. Li, and H. J. Zhang, “A Memory Learning Framework for Effective Image Retrieval,” IEEE Transactions on Image Processing, Vol.14, pp. 511-524, 2005.
[6] P.-Y. Yin, B. Bhanu, K.-C. Chang, and A. Dong, “Improving Retrieval Performance by Long-term Relevance Information,” Proceedings of 16th International Conference on Pattern Recognition, Vol.3, pp. 533-536, 2002.
[7] W. Jiang, G. Er, and Q. Dai, “Multi-Layer Semantic Representation Learning for Image Retrieval,” The IEEE International Conference on Image Processing, 2004.
[8] I. Gondra and D. R. Heisterkamp, “Summarizing Inter-Query in Content-Based Image Retrieval via Incremental Semantic Clustering,” Proceedings of the International Conference on Information Technology: Coding and Computing, Vol.2, pp. 18-22, 2004.
[9] I. Gondra, D. R. Heisterkamp, and J. Peng, “Improving the Initial Image Retrieval Set by Inter-Query Learning with One-Class SVMs,” Proceedings of the 3rd International Conference on Intelligent Systems Design and Applications, pp. 393-402, 2004.
[10] C. C. Hang, In Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, p. 104, 2004.
[11] C. Burges, “Simplified Support Vector Decision Rules,” Proceedings of the 13th International Conference on Machine Learning, 1996.
[12] J. T. Kwok and I. W. Tsang, “The Pre-Image Problem in Kernel Methods,” Proceedings of the 12th International Conference on Machine Learning, 2003.
[13] X. S. Zhou and T. S. Huang, “Small Sample Learning During Multimedia Retrieval using BiasMap,” IEEE Conference on Computer Vision and Pattern Recognition, Vol.1, pp. 11-17, 2001.
[14] X. S. Zhou and T. S. Huang, “Edge-based Structural Features for Content-based Image Retrieval,” Pattern Recognition Letters, Vol.22, pp. 457-468, 2001.
[15] M. Stricker and M. Orengo, “Similarity of Color Images,” Storage and Retrieval for Image and Video Databases III, SPIE Proceedings Vol.2420, pp. 381-392, 1995.
[16] L. Wang, K. L. Chan, and P. Xue, “A Criterion for Optimizing Kernel Parameters in KBDA for Image Retrieval,” IEEE Transactions on Systems, Man, and Cybernetics, Vol.35, pp. 556-562, 2005.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] K.-P. Chung, K. W. Wong, and C. C. Fung, “Reducing User Log Size in an Inter-Query Learning Content-Based Image Retrieval (CBIR) System with a Cluster Merging Approach,” International Joint Conference on Neural Networks, Vancouver, Canada, pp. 2163-2170, 2006.

[2] [2] X. S. Zhou and T. S. Huang, “Relevance Feedback in Image Retrieval: A Comprehensive Review,” ACM Multimedia Systems Journal, Vol.8, pp. 536-544, 2003.

[3] [3] D. R. Heisterkamp, “Building a Latent Semantic Index of an Image Database from Patterns of Relevance Feedback,” Proceedings of International Conference on Pattern Recognition, 4, pp. 134-137, 2002.

[4] [4] M. Li, Z. Chen, and H. J. Zhang, “Statistical Correlation Model for Image Retrieval,” Pattern Recognition, Vol.35, pp. 2687-2693, 2002.

[5] [5] J. Han, K. N. Ngan, M. Li, and H. J. Zhang, “A Memory Learning Framework for Effective Image Retrieval,” IEEE Transactions on Image Processing, Vol.14, pp. 511-524, 2005.

[6] [6] P.-Y. Yin, B. Bhanu, K.-C. Chang, and A. Dong, “Improving Retrieval Performance by Long-term Relevance Information,” Proceedings of 16th International Conference on Pattern Recognition, Vol.3, pp. 533-536, 2002.

[7] [7] W. Jiang, G. Er, and Q. Dai, “Multi-Layer Semantic Representation Learning for Image Retrieval,” The IEEE International Conference on Image Processing, 2004.

[8] [8] I. Gondra and D. R. Heisterkamp, “Summarizing Inter-Query in Content-Based Image Retrieval via Incremental Semantic Clustering,” Proceedings of the International Conference on Information Technology: Coding and Computing, Vol.2, pp. 18-22, 2004.

[9] [9] I. Gondra, D. R. Heisterkamp, and J. Peng, “Improving the Initial Image Retrieval Set by Inter-Query Learning with One-Class SVMs,” Proceedings of the 3rd International Conference on Intelligent Systems Design and Applications, pp. 393-402, 2004.

[10] [10] C. C. Hang, In Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, p. 104, 2004.

[11] [11] C. Burges, “Simplified Support Vector Decision Rules,” Proceedings of the 13th International Conference on Machine Learning, 1996.

[12] [12] J. T. Kwok and I. W. Tsang, “The Pre-Image Problem in Kernel Methods,” Proceedings of the 12th International Conference on Machine Learning, 2003.

[13] [13] X. S. Zhou and T. S. Huang, “Small Sample Learning During Multimedia Retrieval using BiasMap,” IEEE Conference on Computer Vision and Pattern Recognition, Vol.1, pp. 11-17, 2001.

[14] [14] X. S. Zhou and T. S. Huang, “Edge-based Structural Features for Content-based Image Retrieval,” Pattern Recognition Letters, Vol.22, pp. 457-468, 2001.

[15] [15] M. Stricker and M. Orengo, “Similarity of Color Images,” Storage and Retrieval for Image and Video Databases III, SPIE Proceedings Vol.2420, pp. 381-392, 1995.

[16] [16] L. Wang, K. L. Chan, and P. Xue, “A Criterion for Optimizing Kernel Parameters in KBDA for Image Retrieval,” IEEE Transactions on Systems, Man, and Cybernetics, Vol.35, pp. 556-562, 2005.