在現行的以內容為基礎的影像資料庫檢索(Content-Based Image Retrieval)研究中，大多數的系統在搜尋使用者想要的結果時，都只根據在目前這一輪查詢期間(Query Session)的資訊來修正搜尋的結果。然而系統在過往的查詢期間所得到的資訊，應該被善加利用，以利後續的查詢能夠更快地得到更好的結果。

在本篇論文中，我們提出了一個能夠累積過往查詢期間資訊的影像檢索系統。我們將每一個查詢期間視為一個隱含的語意概念(Hidden Semantic Concept)，對於每一個查詢期間，藉由使用者提供的相關回饋(Relevance Feedback)資訊，我們訓練出一個支援向量分類器(Support Vector Machine Classifier)來代表此次查詢期間，並根據訓練出來的支援向量分類器，以機率模型來估算影像中所隱含的語意特徵。影像資料庫中的影像及其所估算得到的隱含語意特徵形成本篇論文中的隱含語意空間(Hidden Semantic Space)。當系統累積過多的查詢期間，也就是為影像所估算的隱含語意特徵過多時，我們利用支援向量群聚法(Support Vector Clustering)，根據現有的隱含語意特徵來對影像做分群。分群完成之後的每一群影像，可視為一個整合過的隱含語意概念。藉由支援向量群聚法，我們重整目前已累積的隱含語意空間，以節省儲存量，最重要的，提供系統一個在檢索上更有效的隱含語意空間。

在本篇論文中所設計的一系列實驗，包含了和參考文獻的比較以及對實驗結果的分析。由實驗結果可以看出，我們所提出的影像隱含語意特徵以及支援向量群聚法，的確在影像檢索系統的效能上，有顯著的提升。

Since intra-session learning in content-base image retrieval (CBIR) system infers user’s preference according to merely the information of current relevance feedback session, many researchers now attempt to accumulate and utilize the knowledge obtained from previous query sessions. This thesis presents a method to infer hidden semantic cues by accumulating the knowledge learned from relevance feedback sessions. We propose to estimate the explicit relations between hidden semantic concepts and images using a probabilistic model. In short-term learning, we apply the general SVM classification to initialize the hidden semantic space. Once the accumulated hidden semantic space becomes impractically large, we propose using support vector clustering (SVC) to construct a more compact and still meaningful hidden semantic space with lower dimensionality. Given a dimension-reduced hidden semantic space, we then perform the image query in terms of the hidden semantic attributes instead of merely the visual features. Our experimental results and comparisons demonstrate that the proposed hidden semantic feature representation as well as the SVC-based technique indeed achieves promising results.

6. REFERENCES

[1] Y. Ishikawa, R. Subramanya, and C. Faloutsos, “MindReader: Query databases through multiple examples,” in Proc. 24th Int. Conf. Very Large DataBases, New York, 1998, pp. 218-227.
[2] I. J. Cox, M. L. Miller, T. P. Minka, T. V. Papathomas, and P. N. Yianilos, “The Bayesian Image Retrieval System, PicHunter: Theory, Implementation, and Psychophysical Experiments,” IEEE Trans. Image Processing, vol. 9, pp. 20-37, Jan. 2000.
[3] X. Zhou, Q. Zhang, L. Liu, L. Zhang, and B. Shi, “An image retrieval method based on analysis of feedback sequence log,” Pattern Recognition Letters, vol. 24, no. 14, pp. 2499-2508, 2003.
[4] I. Bartolini, P. Ciaccia, and F. Waas, “FeedbackBypass: A New Approach to Interactive Similarity Query Processing,” in Proc. 27th Very Large Databases, Roma, Italy, 2001.
[5] J. Fournier and M. Cord, “Long-term similarity learning in content-based image retrieval,” Proc. ICIP, 2002.
[6] H. Y. Bang, C. Zhang, and T. Chen, “Semantic Propagation from Relevance Feedbacks,” Proc. ICME, 2004.
[7] X. He, O. King, W.Y. Ma, M. Li, and H.J. Zhang, “Learning a semantic space from user’s relevance feedback for image retrieval,” IEEE Trans. Circuit and Systems for Video Technology, vol. 13, no. 1, pp. 39-48, Jan. 2003.
[8] A. Dong and Bir Bhanu, “A new semisupervised EM algorithm for image retrieval,” Proc. CVPR, 2003.
[9] S. Ghebreab, C. Carl Jaffe, and Arnold W. M. Smeulders, “Population-Based Incremental Interactive Concept Learning for Image Retrieval by Stochastic String Segmentations,” IEEE Trans. Medical Imaging, vol. 23, no. 6, pp. 676-689, Jun. 2004.
[10] P. H. Lewis, K. Martinez, F. S. Abas, M. F. A. Fauzi, S. C. Y. Chan,
M. J. Addis, M. J. Boniface, P. Grimwood, A. Stevenson, C. Lahanier, and J. Stevenson, “An Integrated Content and Metadata Based Retrieval System for Art,” IEEE Trans. Image Processing, vol. 13, no. 3, pp. 302-313, Mar. 2004.
[11] N. Cristianini, and J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press 2000.
[12] Alberto Del Bimbo, Visual information retrieval, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1999.
[13] L.G. Shapiro and G. C. Stockman, Computer vision, Prentice Hall, 2001.
[14] J. Li, J. Z. Wang, and G. Wiederhold, "IRM: integrated region matching for image retrieval," Proc. ACM International Conference on Multimedia, 2000.
[15] D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 5, pp. 603-619, May 2002.
[16] A. Ben-Hur, D. Horn, H.T. Siegelmann, and V. Vapnik, "Support Vector Clustering," Journal of Machine Learning Research, vol. 2, pp. 125-137, 2001.
[17] J. Yang, V. Estivill-Castro, and S. Chalup, “Support vector clustering through proximity graph modeling,” Proc. ICONIP, vol. 2, pp. 898-903, 2002.
[18] http://www.ics.uci.edu/~mlearn/MLSummary.html.
[19] Arnold W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-Based Image Retreival at the End of the Early Years,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349-1380, Dec. 2000.
[20] J. Li, and James Z. Wang, “Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1075-1088, Sep. 2003.
[21] G. Giacinto, and F. Goli, “Bayesian Relevance Feedback for Content-Based Image Retrieval,” Pattern Recognition, vol. 37, issue 7, pp. 1499-1508, July, 2004.
[22] R. Zhang, and Z. Zhang, “Stretching Bayesian Learning in the Relevance Feedback of Image Retrieval,” vol. 3, pp. 355-367, ECCV 2004.
[23] J. Bi, K. P. Bennett, M. Embrechts, C. M. Breneman and M. Song, "Dimensionality Reduction via Sparse Support Vector Machines," Journal of Machine Learning Research, vol. 3, pp. 1229-1243, 2003.
[24] J. C. Platt, “Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods,” in Advances in Large Margin Classifiers, Cambridge, MA: MIT Press, 2000, pp. 61-74.