隨著網路技術的進步與行動裝置的廣泛使用，不僅可以讓使用者可以即時獲取所需的資訊，而且還縮短了人與人之間的距離。如今人們可以在拍照後立即輕鬆地在網際網路上共享他們的照片。隨著線上影像的與日俱增，發現影像中之視覺概念是一個具有挑戰性的問題。本研究主要探討如何使電腦可以理解任意一張影像並明白影像中所包含的視覺概念。為了解決這個問題，大部分前人研究試圖將不同的影像分類至預先定義好的許多類別中；然而因線上影像的快速成長，影像的類別將無法完全預先設定，所以我們利用影像中類似的影像區域而不是分類模型來解決這個問題。不同於將這個問題作為一個分類的工作，試圖把不同的影像區分至預先定義的某個類別中，我們建議在這項工作開始尋找類似的形象區塊。具體來說，我們採用關聯式規則這項技術建立影像之間的關係，從線上影像中發掘相同的視覺概念，並找出具有相同視覺概念之影像。

With the advance of networking technologies, the widespread use of handheld devices not only enables instant access of required information but also shortens the distance among acquaintances. Nowadays one can easily share their photos on the Internet right after the photos are taken. With an increasing amount of images, a challenging problem for computer algorithms is to discover the visual-concept embedded within an image. Instead of modeling this problem as a classification process which attempts to categorize images into different pre-defined classes, we propose in this work to start with identifying similar image patches. Specifically, we adopt the technique of mining association rules to construct evident relationships among image patches so as to discover identical visual-concepts from online images.

[1] M. Agrawal, K. Konolige, and M. Blas, "CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching," Proceedings of 10th European Conference on Computer Vision, pp. 102-115, Octomber 2008.
[2] R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules between Sets of Items in Large Databases,” the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207-216, May 1993.
[3] E. Barbu, P. Heroux, S. Adam, and E. Trupin, “Indexation of Document Images Using Frequent Items,” The Fifth International Workshop on Pattern Recognition in Information Systems (PRIS), pp. 164-173, May 2005.
[4] Bing, http://www.bing.com/.
[5] D. Byrne, A. Doherty, C. Snoek, G. Jones, and A. Smeaton, “Everyday Concept Detection in Visual Lifelogs: Validation, Relationships and Trends,” Multimedia Tools and Applications, 49(1): 119-144, August 2010.
[6] G. Csurka, C. Bray, C. Dance, and L. Fan, "Visual Categorization with Bags of Keypoints," Proceedings of European Conference on Computer Vision Workshop on Statistical Learning in Computer Vision pp. 59-74, May 2004.
[7] R. Datta, D. Joshi, J. Li, and J. Z. Wang, “Image Retrieval: Ideas, Influences, and Trends of the New Age,” ACM Computing Surveys, 40(2): 1-60, April 2008.
[8] J. Deng, A. C. Berg, K. Li, and L. Fei-Fei, “What Does Classifying More Than 10,000 Image Categories Tell Us?,” Proceedings of the 11th European conference on Computer vision, pp. 71-84, September 2010.
[9] C. Djeraba, M. Bouet, and H. Briand, "Concept-Based Query in Visual Information Systems," Proceedings of Proceedings IEEE International Forum on Research and Technology Advances in Digital Libraries, 1998 (ADL 98) pp. 299-308, April 1998.
[10] A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth, "Describing Objects by Their Attributes," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1778-1785, June 2009.
[11] V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid, “Groups of Adjacent Contour Segments for Object Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(1): 36-51, January 2008.
[12] M. Flickner, H. Sawhney, W. Niblack, J. Ahsley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steel, and P. Yanker, “Query by Image and Video Content: The QBIC System,” IEEE Computer, 28(9): 23-32, September 1999.
[13] Flickr, http://www.flickr.com/.
[14] M. Fritz, G. J. M. Kruijff, and B. Schiele, “Tutor-Based Learning of Visual Categories Using Different Levels of Supervision,” Computer Vision and Image Understanding, 114(5): 564-573, May 2010.
[15] B. Funt, K. Barnard, and L. Matin, "Is Machine Colour Constancy Good Enough?," Proceedings of the 5th European Conference on Computer Vision, pp. 445-459, June 1998.
[16] GazoPa, http://www.gazopa.com/.
[17] V. N. Gudivada, and V. V. Raghavan, “Content Based Image Retrieval Systems,” IEEE Computer, 28(9): 18-22, September 1995.
[18] M. J. Huiskes, and M. S. Lew, “The MIR Flickr Retrieval Evaluation,” Proceeding of the 1st ACM International Conference on Multimedia Information Retrieval, pp. 39-43, October 2008.
[19] M. J. Huiskes, B. Thomee, and M. S. Lew, “New Trends and Ideas in Visual Concept Detection: the MIR Flickr Retrieval Evaluation Initiative,” Proceedings of the 11th ACM International Conference on Multimedia Information Retrieval (MIR), pp. 527-536, March 2010.
[20] Google Similar Images, http://similar-images.googlelabs.com/.
[21] C. v. G. Jan, “Visual Word Ambiguity,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(7): 1271-1283, July 2010.
[22] G. Ke, L. Shouxun, G. Junbo, Z. Dongming, Z. Yongdong, and W. Yufeng, "Object Retrieval Based on Spatially Frequent Items with Informative Patches," Proceedings of 2008 IEEE International Conference on Multimedia and Expo, pp. 1305-1308, June 2008.
[23] S. Lazebnik, C. Schmid, and J. Ponce, “Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories,” 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2169-2178, June 2006.
[24] L.-J. Li, R. Socher, and F.-F. Li, "Towards Total Scene Understanding: Classification, Annotation and Segmentation in an Automatic Framework," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2036-2043, June 2009.
[25] Y. Liu, D. Zhang, G. Lu, and W.-Y. Ma, “A Survey of Content-Based Image Retrieval with High-Level Semantics,” Pattern Recognition, 40(1): 262-282, January 2007.
[26] D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Jounal of Computer Vision, 60(2): 91-110, November 2004.
[27] D. G. Lowe, "Object Recognition from Local Scale-Invariant Features," Proceedings of the Seventh IEEE International Conference on Computer Vision, pp. 1150-1157, September 1999.
[28] K. Mikolajczyk, and C. Schmid, “Scale and Affine Invariant Interest Point Detectors,” International Journal of Computer Vision (IJCV), 60(1): 63-86, October 2004.
[29] P. Mylonas, E. Spyrou, Y. Avrithis, and S. Kollias, “Using Visual Context and Region Semantics for High-Level Concept Detection,” IEEE Transactions on Multimedia, 11(2): 229-243, February 2009.
[30] Q. Nie, S.-Y. Zhan, and J.-X. Su, "Image Classification Technology Based on Mining of Frequent Item Sets," Proceedings of Chinese Conference on Pattern Recognition, 2008 (CCPR 2008), pp. 1-5, October 2008.
[31] D. Nister, and H. Stewenius, "Scalable Recognition with a Vocabulary Tree," Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2161-2168, June 2006.
[32] B. Ommer, and J. M. Buhmann, “Learning the Compositional Nature of Visual Object Categories for Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3): 501-516, March 2010.
[33] J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, "Lost in Quantization: Improving Particular Object Retrieval in Large Scale Image Databases," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, June 2008.
[34] J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, "Object Retrieval with Large Vocabularies and Fast Spatial Matching," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, June 2007.
[35] B. Russell, A. Torralba, K. Murphy, and W. Freeman, “LabelMe: A Database and Web-Based Tool for Image Annotation,” International Journal of Computer Vision, 77(1): 157-173, May 2008.
[36] J. Shotton, A. Blake, and R. Cipolla, "Contour-Based Learning for Object Detection," Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05), pp. 503-510, October 2005.
[37] J. Sivic, and A. Zisserman, "Video Google: A Text Retrieval Approach to Object Matching in Videos," Proceedings of Ninth IEEE International Conference on Computer Vision, pp. 1470-1477, October 2003.
[38] C. G. M. Snoek, and A. W. M. Smeulders, “Visual-Concept Search Solved?,” IEEE Computer, 43(6): 76-78, June 2010.
[39] TinEye, http://www.tineye.com/.
[40] A. Torralba, R. Fergus, and W. T. Freeman, “80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(11): 1958-1970, November 2008.
[41] P. Turcot, and D. G. Lowe, "Better Matching with Fewer Features: The Selection of Useful Features in Large Database Recognition Problems," Proceedings of 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 2109-2116, September 2009.
[42] K. van de Sande, T. Gevers, and C. Snoek, "Evaluation of Color Descriptors for Object and Scene Recognition," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, June 2008.
[43] N. Vasconcelos, “From Pixels to Semantic Spaces: Advances in Content-Based Image Retrieval,” IEEE Computer, 40(7): 20-26, July 2007
[44] R. Veltkamp, and M. Tanase, "Content-Based Image Retrieval Systems: A Survey," Technical Report UU-CS-2000-34, October 2000.
[45] VisualSeeker-YahooJapanラボ, “VisualSeeker.”
[46] S. Xiaodan, L. Ching-Yung, and S. Ming-Ting, "Autonomous Learning of Visual Concept Models," Proceedings of IEEE International Symposium on Circuits and Systems, 2005 (ISCAS 2005), pp. 4598-4601, May 2005.
[47] T. Xiaofeng, L. Duan, L. Hanqing, C. Xu, Q. Tian, and J. S. Jin, "A Mid-Level Visual Concept Generation Framework for Sports Analysis," Proceedings of IEEE International Conference on Multimedia and Expo, 2005 (ICME 2005), pp. 646-649, July 2005.
[48] P.-Y. Yin, and S.-H. Li, “Content-Based Image Retrieval Using Association Rule Mining with Soft Relevance Feedback,” Journal of Visual Communication and Image Representation, 17(5): 1108-1125, April 2006.
[49] J. Yu, and J. Luo, “Leveraging Probabilistic Season and Location Context Models for Scene Understanding,” Proceedings of the 2008 International Conference on Content-Based Image and Video Retrieval, pp. 169-178, July 2008.
[50] E. Zavesky, L. Zhu, D. Gibbon, and B. Shahraray, "Searching Visual Semantic Spaces with Concept Filters," Proceedings of International Conference on Semantic Computing, 2007. (ICSC 2007), pp. 329-336, September 2007.