臺灣博碩士論文加值系統

為因應大量圖片的累積所伴隨而來的管理需求，在本論文裡我們提出了數種數位圖庫及個人數位相簿的管理技術。首先，對於數位圖庫的管理，我們提出了一個新的擷取色彩特徵的方法，此方法可保有原圖片的色彩分佈。此方法包含有FC和VC，是以BQMP為基礎的擷取方法，可保有圖片的色彩分佈並減少擷取過理中所造成的失真。除了以EMD作為此色彩特徵的距離函式，我們提出了一個快速且有效的距離函式，稱為CHIC，我們也探討了如何有效率地實作此距離函式。最後我們以內容為基礎的圖片搜取應用來評估此色彩特徵擷取方法的實用性。

此外，在本論文裡，我們也對個人數位相簿管理的問題深感興趣。我們首先探討了個人行動裝置上線上事件偵測的問題，有別於前人的研究所作之桌上型電腦的假設，我們著眼於在個人行動裝置上支援此應用。我們提出了一個同時考慮時間和地點的事件偵測方法。我們以Poisson隨機函式並加以適當的限制來模式化使用者的照相行為，當以此函式所作之偵測決定的可信度不夠高時，地點資訊會被一並納入考慮。

之後，我們探討了如何找出一組代表性的圖片。此問題為接續著事件偵測之後的下一個階段問題，找出一組代表性的圖片可以幫助使用者了解個人相簿的組織結構。我們認為一組代表性的圖片必須是每一張圖片儘量具代表性同時相鄰之間的圖片極具差異性。針對提供個人行動裝置上的應用，我們將此問題模式化成一個最佳化的問題並以Viterbi演算法求其最佳解。我們也進一步將此問題一般化於提供桌上型電腦上應用的支援。

最後，我們討論了個人相簿的圖片分類的問題。我們提出一個新的架構以解決此問題，它利用PAM叢集方法以幫助使用者標示學習資料，並以改進式的分類樹方法分類圖片，因為對使用者而言分類樹結構是易於呈現而了解的，此外，為了讓使用者對分類樹的結構給予回饋，我們將每一個節點連結一個代表圖以呈現分類結果。

Motivated by the need of managing a large number of images, we propose in this dissertation several techniques to support the management of personal photo collections and general image repositories. First, we propose methods that extract color features by preserving the image color distribution for the management of general image repositories. Based on the Binary Quaternion-Moment-Preserving thresholding technique, the proposed extraction methods, Fixed Cardinality and Variable Cardinality, are able to extract color features by preserving the color distribution of images and to substantially reduce the distortion incurred in the extraction process. In addition to utilizing the Earth Mover''s Distance as the distance measure of our color features, we also devise an efficient and effective distance measure, Comparing HIstograms by Clustering. Moreover, the efficient implementation of our extraction methods is explored. We then evaluate the meaningfulness of the new extraction methods by the application to content-based image retrieval.

In addition, we are interested in the problem of managing personal photo collections. We first explore the problem of online event detection on a mobile platform as opposed to most prior works in which a desktop platform is assumed. We propose an event detection algorithm that fuses time and location information, which is deemed the important information for personal photo management. Specifically, we model event occurrences in a user''s photo-taking behavior as a Poisson process by imposing certain constraints on calculating the elapsed time. Location information is incorporated into event detection when confidence in a decision based on the Poisson process is not high enough.

We next explore the problem of selecting a set of representative photos. Selection of representative photos can be deemed as the next step after event detection because a good selection can help users indicate the organizational structure of a personal photo collection. We consider that a good selection is one in which the selected photos are as representative as possible of their individual group and maximally distinct from one another in the distinction window. Motivated by the need for photo management support on mobile platforms, we formulate the problem as an optimization problem and solve it by the Viterbi algorithm. We further generalize the problem and propose appropriate algorithms to support photo management on desktop computers.

We also study the problem of image classification on personal photo collections. We propose a framework, User-assisted image Classification on Personal photo collections, to address this problem, where the PAM clustering method is used to help a user label the training data. A modified decision tree classifier is proposed to classify photos because a decision tree classifier is easily understood by a user. In addition, we visualize the classification results by associating a tree node with a representative image, which facilitates a user to give feedback to our decision tree classifier.

1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Overview of the dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Adaptive color feature extraction . . . . . . . . . . . . . . . . . . . . 3
1.2.2 Online event detection on personal photo collections . . . . . . . . . . 4
1.2.3 Optimal selection of representative photos . . . . . . . . . . . . . . . 5
1.2.4 Image classification on personal photo collections . . . . . . . . . . . 5
1.3 Organization of the dissertation . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Adaptive Color Feature Extraction 8
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Color feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 The BQMP thresholding technique . . . . . . . . . . . . . . . . . . . 13
2.2.3 The EarthMover’sDistance . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 Adaptive color feature extraction . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.1 Fixed cardinality and variable cardinality . . . . . . . . . . . . . . . . 16
2.3.2 Comparison with the binningmethods . . . . . . . . . . . . . . . . . 19
2.3.3 Comparison with vector quantization . . . . . . . . . . . . . . . . . . 20
2.4 Distance measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4.1 CHIC : Comparing HIstograms by Clustering . . . . . . . . . . . . . 21
2.4.2 Comparison between CHIC and EMD. . . . . . . . . . . . . . . . . . 25
2.5 Efficiency consideration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.5.1 Swappingmechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.5.2 Algorithm modification . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.6 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.6.1 Retrieval precision . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.6.2 Execution time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.6.3 Interpretation on colormoments . . . . . . . . . . . . . . . . . . . . . 37
2.7 Summary and future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3 Online Event Detection on Personal Photo Collections 40
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.3 Time and location informationmodeling . . . . . . . . . . . . . . . . . . . . 45
3.3.1 Modeling temporal event occurrence as a Poisson process . . . . . . . 46
3.3.2 Location informationmodeling . . . . . . . . . . . . . . . . . . . . . 49
3.4 Event detection in real time . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.4.1 Problemformulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.4.2 Afully automatic approach . . . . . . . . . . . . . . . . . . . . . . . 51
3.4.3 Parameter-update . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.4.4 User feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.5 User study and experimental results . . . . . . . . . . . . . . . . . . . . . . . 57
3.5.1 User study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.5.2 Evaluation of the Poisson processmodeling . . . . . . . . . . . . . . . 59
3.5.3 Evaluation of the automatic approach . . . . . . . . . . . . . . . . . . 61
3.5.4 User feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.5.5 Analysis ofmisdetection . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.5.6 Joint consideration with visual features . . . . . . . . . . . . . . . . . 65
3.6 Summary and future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4 Optimal Selection of Representative Photos 69
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.3 Optimal selection of representatives on mobile devices . . . . . . . . . . . . . 75
4.3.1 Problemformulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.3.2 Distinction and representativenessmeasure . . . . . . . . . . . . . . . 78
4.3.3 Use of theViterbi algorithm . . . . . . . . . . . . . . . . . . . . . . . 79
4.3.4 Incrementality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.3.5 Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.4 Generalization of OSR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.4.1 Multiple representatives in a segment . . . . . . . . . . . . . . . . . . 84
4.4.2 Distinction between representatives in a window . . . . . . . . . . . . 86
4.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.5.1 Data preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.5.2 OSRonmobile devices . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.5.3 OSRon desktop computers . . . . . . . . . . . . . . . . . . . . . . . 93
4.6 Summary and future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.7 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.8 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
4.9 User-assisted labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.9.1 Problemformulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.9.2 Image representation . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.9.3 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.10 Classification and user feedback . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.10.1 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.10.2 Visualization and user feedback . . . . . . . . . . . . . . . . . . . . . 109
4.11 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
4.11.1 Evaluation of the clustering phase . . . . . . . . . . . . . . . . . . . . 113
4.11.2 Evaluation of the classification phase . . . . . . . . . . . . . . . . . . 114
4.12 Summary and future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5 Conclusions 118

[1] http://www.gpsw.co.uk/index.html.
[2] Digital still camera image file format standard. In Japan Electronic Industry Development Association, 1998.
[3] R. Agrawal, S. Ghosh, T. Imielinski, B. Iyer, and A. Swami. An interval classifier for database mining applications. In Proc. of the Very Large Data Bases Conf., pages 560—573, 1992.
[4] S. Antani, R. Kasturi, and R. Jain. A survey on the use of pattern recognition methods for abstraction, indexing, and retrieval of images and video. Pattern Recognition,
35(4):945—965, 2002.
[5] I. Assent, A. Wenning, and T. Seidl. Approximation techniques for indexing the earth mover’s distance in multimedia databases. In Proc. of the IEEE Int’l Conf. on Data Engineering, 2006.
[6] M. Bellare. Interactive proofs and approximation: Reduction from two provers in one round. In ISTCS, pages 266—274, 1993.
[7] M. Boutell, A. Choudhury, J. Luo, and C. M. Brown. Using semantic features for scene classification: How good do they need to be? In Proc. of the IEEE Int’l Conf. on Multimedia and Expo, pages 785—788, 2006.
[8] M. Boutell and J. Luo. Incorporating temporal context with content for classifying image collections. In Proc. of the Int’l Conf. on Pattern Recognition, pages 947— 950,
2004.
[9] T. Bozkaya and M. Ozsoyoglu. Distance-based indexing for high-dimensional metric spaces. In Proc. of the ACMInt’l Conf. onManagement of Data, pages 357—368, 1997.
[10] L. Brown and L. Gruenwald. Tree-Based Indexes for Image Data. Visual Comm. and Image Representation, 9 (4):300—313, 1998.
[11] J.-Y. Chen, C. A. Bouman, and J. C. Dalton. Similarity pyramids for browsing and organization of large image databases. In Proc. of the SPIE, Human Vision and Electronic
Imaging III , volume 3299, pages 563—575, 1998.
[12] M.-S. Chen, J. Han, and P. S. Yu. Data mining: An overview from database perspective. IEEE Trans. on Knowledge and Data Engineering, 5(1):866—883, Dec. 1996.
[13] W.-T. Chen and M.-S. Chen. Event Detection in Real Time on a Personal Camera Phone Image Collection. To appear in the ACM Multimedia Systems Journal.
[14] W.-T. Chen, Y.-L. Chen, and M.-S. Chen. Mining frequent spatial patterns in image databases. In Proc. of the Pacific-Asia Conference on Knowledge Discovery and Data
Mining, pages 699—703, 2006.
[15] W.-T. Chen, Y.-H.Wen, andM.-S. Chen. User-assisted image classification on personal photo collections. In Proc. of the IEEE Int’l Conf. on Multimedia and Expo, pages
1945—1948, 2006.
[16] P. Ciaccia, M. Patella, and P. Zezula. M-tree: an efficient access method for similarity search in metric spaces. In Proc. of the Very Large Data Bases Conf., 1997.
[17] M. Cooper and J. Foote. Discriminative techniques for keyframe selection. In Proc. of the IEEE Int’l Conf. on Multimedia and Expo, pages 502—505, 2005.
[18] M. Cooper, J. Foote, A. Girgensohn, and L. Wilcox. Temporal event clustering for digital photo collections. In Proc. of the ACM Int’l Conf. on Multimedia, pages 364—
373, 2003.
[19] R. B. D’Agostino and M. A. Stephens. Goodness-Of-Fit Techniques. Marcel Dekker, 1986.
[20] R. Datta, W. Ge, J. Li, and J. Wang. Toward bridging the annotation-retrieval gap in image search. In Proc. of the ACM Int’l Conf. on Multimedia, 2006.
[21] F. Dawson, S. Mansour, and S. Silverberg. iCalendar Message-based Interoperability Protocol. In RFC 2447, 1998.
[22] W. H. Day and H. Edelsbrunner. Efficient algorithms for agglomerative hierarchical clustering methods. Journal of Classification, 1:1—24, 1984.
[23] D. Defays. An effecient alogrithm for a complete link method. The Computer Journal, 20(4):364—366, 1977.
[24] M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker. Query by image and video content: The QBIC system. IEEE Computer, 1908, 28(9):23—32, September 1995.
[25] G. D. FORNEY. The viterbi algorithm. IEEE Proceedings, 61:268—278, Mar. 1973.
[26] J. B. Fraleigh. A First Course in Abstract Algebra. Addison-Wesley, 1982.
[27] Hichem Frigui. Visualizing and Browsing Large Image Databases. In Proc. of the Int’l Conf. on Information and Knowledge Engineering, pages 68—74, 2004.
[28] D. Frohlich, A. Kuchinsky, C. Pering, A. Don, and S. Ariss. Requirements for photoware. In Proc. of the ACM Int’l Conf. on Computer Supported Cooperative Work, pages 166—175, 2002.
[29] M. Gelgon and K. Tilhou. Structuring the personal multimedia collection of a mobile device user based on geolocation. In Proc. of the IEEE Int’l Conf. on Multimedia and Expo, volume 2, pages 241— 244, 2002.
[30] S. Ghahramani. Fundamentals of Probability. Prentice Hall, 1999.
[31] K. Goh, E. Chang, and K. Cheng. SVM binary classifier ensembles for image classifi-cation. In Proc. of the Int’l Conf. on Information and Knowledge Management, pages 395—402, 2001.
[32] A. Graham, H. Garcia-Molina, A. Paepcke, and T. Winograd. Time as essence for photo browsing through personal digital libraries. In Proc. of the ACM/IEEE Joint
Conf. on Digital libraries, pages 326—335, 2002.
[33] R. M. Gray and D. L. Neuhoff. Quantization. IEEE Trans. on Information Theory, 44(6):2325—2383, 1998.
[34] C. Gurrin, G. Jones, H. Lee, N. N. O’Hare, A.F. Smeaton, and N. Murphy. Mobile access to personal digital photograph archives. In Proc. of the ACM Int’l Conf. on
MobileHCI, pages 19—22, 2005.
[35] S. Harada, M. Naaman, Y. J. Song, Q.-Y. Wang, and A. Paepcke. Lost in memories:interacting with photo collections on pdas. In Proc. of the ACM/IEEE Joint Conf. on
Digital libraries, pages 325—333, 2004.
[36] 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. on Circuits and Systems for
Video Technology, 13:39—48, Jan. 2003.
[37] F. S. Hiller and G. J. Liberman. Introduction to Mathematical Programming. MaGraw-Hill, 1990.
[38] W. Hsu, M. L. Lee, and J. Zhang. Image mining: Trends and developments. Journal of Intelligent Information Systems, 19:7—23, Jul. 2002.
[39] X.-S. Hua and S.-P. Li. Personal media sharing and authoring on the web. In Proc. of the ACM Int’l Conf. on Multimedia, pages 375—378, 2005.
[40] X.-S. Hua, L. Lu, and H.-J. Zhang. Ave: automated home video editing. In Proc.of the ACM Int’l Conf. on Multimedia, pages 490—497, 2003.
[41] X.-S. Hua, L. Lu, and H.-J. Zhang. Optimization-based automated home video editing system. IEEE Trans. on Circuits System for Video Technology, 14(5):572—583, 2004.
[42] J. Huang, S. R. Kumar, M. Mitra, W.-J. Zhu, and R. Zabih. Image indexing using color correlograms. In Proc. of the Conf. on Computer Vision and Pattern Recognition, pages 762—768, 1997.
[43] J. Huang, S. R. Kumar, and R. Zabih. An Automatic Hierarchical Image Classification Scheme. In Proc. of the ACM Int’l Conf. on Multimedia, pages 219—228, 1998.
[44] K. Jarrah, M. Kyan, S. Krishnan, and L. Guan. Computational intelligence techniques and their applications in content-based image retrieval. In Proc. of the IEEE Int’l Conf. on Multimedia and Expo, pages 33—36, 2006.
[45] Y. Jin, L. Khan, L. Wang, and M. Awad. Image annotations by combining multiple evidence & wordnet. In Proc. of the ACM Int’l Conf. on Multimedia, pages 706—715,
2005.
[46] F. Jing, C. Wang, Y. Yao, K. Deng, L. Zhang, and W.-Y. Ma. IGroup: Web Image Search Results Clustering. In Proc. of the ACM Int’l Conf. on Multimedia, 2006.
[47] H.-W. Kang and X.-S. Hua. To learn representativeness of video frames. In Proc. of the ACM Int’l Conf. on Multimedia, pages 423—426, 2005.
[48] L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. New York: JohnWiley &Sons, 1990.
[49] S.M. Kay. Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory. Prentice Hall PTR, 1993.
[50] B. King. Step-wise clustering procedures. J. Am. Stat. Assoc., 69:86—101, 1967.
[51] V. Klee and G. Minty. How Good is the Simplex Algorithm. In Inequalities, volume 3, pages 159—175, 1972.
[52] A. Kushki, P. Androutsos, K. N. Plataniotis, and A. N. Venetsanopoulos. Query Feedback for Interactive Image Retrieval. IEEE Trans. on Circuits and Systems for Video Technology, 14(5):644—655, May 2004.
[53] Y. Li, T. Zhang, and D. Tretter. An overview of video abstraction techniques. In HP Technical Reports, HPL-2001-191, pages 1—23, 2001.
[54] H. Liu, X. Xie, W.-Y. Ma, and H.-J. Zhang. Automatic browsing of large pictures on mobile devices. In Proc. of the ACM Int’l Conf. on Multimedia, pages 148—155, 2003.
[55] F. Long, H.J. Zhang, and D. D. Feng. Fundamentals of content-based image retrieval. InMultimedia Information Retrieval and Management Technological Fundamentals and
Applications. Springer, 2003.
[56] J. Luo, A. Singhal, and A. E. Savakis. Efficient mobile imaging using emphasis image selection. In PICS, pages 355—359, 2003.
[57] W.-Y.Ma and B. S.Manjunath. Netra: A toolbox for navigating large image databases. In Proc. of the IEEE Int. Conf. on Image Processing, volume 1, pages 568—571, Oct.
1997.
[58] Y.-F. Ma, L. Lu, H.-J. Zhang, and M.-J. Li. A user attention model for video summarization. In Proc. of the ACM Int’l Conf. on Multimedia, pages 533—542, 2002.
[59] M. K. Mandal, T. Aboulnasr, and S. Panchanathan. Image indexing using moments and wavelets. IEEE Trans.on Consumer Electronics, 42(3):557—565, August 1996.
[60] B. S. Manjunath and W.-Y. Ma. Texture features for browsing and retrieval of image data. IEEE Trans. on Pattern Anal. and Mach. Intel., 18(8):837—842, Aug. 1996.
[61] T. Mei, X.-S. Hua, and H.-Q. Zhou. Tracking users’ capture intention: a novel complementary view for home video content analysis. In Proc. of the ACM Int’l Conf. on
Multimedia, pages 531—534, 2005.
[62] M. Naaman, S. Harada, Q.-Y. Wang, H. Garcia-Molina, and A. Paepcke. Context data in geo-referenced digital photo collections. In Proc. of the ACM Int’l Conf. on
Multimedia, pages 196—203, 2004.
[63] M. Naaman, Y. J. Song, A. Paepcke, and H. Garcia-Molina. Automatic organization for digital photographs with geographic coordinates. In Proc. of the ACM/IEEE Joint
Conf. on Digital libraries, pages 53—62, 2004.
[64] M. Naaman, R. B. Yeh, H. Garcia-Molina, and A. Paepcke. Leveraging context to resolve identity in photo albums. In Proc. of the ACM/IEEE Joint Conf. on Digital
libraries, pages 178—187, 2005.
[65] M. Oge and F. Borko. Muse: A content-based image search and retrieval system using relevance feedback. Multimedia Tools Appl., 17:21—50, 2002.
[66] N. O’Hare, C. Gurrin, H. Lee, N. Murphy, A. F. Smeaton, and G. J.F. Jones. My digital photos: where and when? In Proc. of the ACM Int’l Conf. on Multimedia,
pages 261—262, 2005.
[67] A. Papoulis and S. U. Pillair. Probability, random variables, and stochastic processes. McGraw-Hill, 2002.
[68] V. Paxson and S. Floyd. Wide area traffic: the failure of Poisson modeling. IEEE/ACM Trans. on Networking, 3:226—244, Jun. 1995.
[69] S.-C. Pei and C.-M. Cheng. Color image processing by using binary quaternion moment-preserving thresholding technique. IEEE Trans. on Image Processing, 8(5):614—628, May 1999.
[70] A. Pentland, R.W. Picard, and S. Sclaroff. Photobook: Content-based manipulation of image databases. In Proc. of the SPIE Storage Retrieval Image Video Database II, pages 34—47, Feb. 1994.
[71] A. Pigeau and M. Gelgon. Incremental statistical geo-temporal structuring of a personal camera phone image collection. In Proc. of the Int’l Conf. on Pattern Recognition, pages 878—881, 2004.
[72] A. Pigeau and M. Gelgon. Building and tracking hierarchical geographical & temporal partitions for image collection management on mobie devices. In Proc. of the ACM
Int’l Conf. on Multimedia, pages 141—150, 2005.
[73] J. C. Platt, M. Czerwinski, and B. A. Field. Phototoc: Automatic clustering for browsing personal photographs. In Proc. of the ICICS-PCM, 2003.
[74] F. Qian, M. Li, L. Zhang, H.-J. Zhang, and B. Zhang. Gaussian mixture model for relevance feedback in image retrieval. In Proc. of the IEEE Int’l Conf. on Multimedia
and Expo, volume 1, pages 229—232, 2002.
[75] J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81—106, 1986.
[76] K. Rodden and K. R. Wood. How do people manage their digital photographs? Proc. of the ACM Int’l Conf. on CHI, 4:409—416, Apr. 2003.
[77] Y. Rubner. Perceptual Metrics for Image Database Navigation. In Ph. D. thesis, 1999.
[78] Y. Rubner, C. Tomasi, and L. J. Guibas. A Metric for Distributions with Applications to Image Databases. In Proc. of the IEEE Int’l Conf. on Computer Vision, page 59,
1998.
[79] Y. Rui and T. Huang. Optimizing learning in image retrieval. Proc. of the IEEE Conf. Computer Vision and Pattern Recognition, 1:236—243, 2000.
[80] Y. Rui, T. S. Huang, and S.-F. Chang. Image Retrieval: current techniques, promising directions, and open issues. Journal of Visual Communication and Image Representation,
10(1), Mar. 1999.
[81] Y. Rui, T. S. Huang, M. Ortega, and S. Mehrotra. Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans. on Circuits and Systems
for Video Technology, 8:644—655, 1998.
[82] R. Sarvas, M. Viikari, J. Pesonen, and H. Nevanlinna. Mobshare: Controlled and immediate sharing of mobile images. In Proc. of the ACM Int’l Conf. on Multimedia, pages 724—731, 2004.
[83] R. Singh, R. Knickmeyer, P. Gupta, and R. Jain. Designing experiential environments for management of personal multimedia. In Proc. of the ACM Int’l Conf. on
Multimedia, pages 496—499, 2004.
[84] A. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Trans. on Pattern Anal.and Machine Intell., 22(12):1349—1380, Dec. 2000.
[85] J. R. Smith and S.-F. Chang. VisualSeek: a fully automated content-based image query system. In Proc. of the ACM Int’l Conf. on Multimedia, pages 87—98, November
1996.
[86] J. R. Smith and C. Li. Image classification and querying using composite region templates. Computer Vision and Image Understanding, 75:165—174, 1999.
[87] M. Stricker and M. Orengo. Similarity of color images. In Storage and Retrieval for Image and Video Databases (SPIE), pages 381—392, 1995.
[88] M. J. Swain and D. H. Ballard. Color indexing. Int’l Journal of Computer Vision, 7:11—32, 1991.
[89] M. Szummer and R. Picard. Indoor-outdoor image classification. IEEE Int’l Workshop on Content-based Access of Images and Video Databases, pages 42—51, 1998.
[90] K. Toyama, R. Logan, and A. Roseway. Geographic location tags on digital images. In Proc. of the ACM Int’l Conf. on Multimedia, pages 156—166, 2003.
[91] A. Vailaya, M. A. T. Figueiredo, A. K. Jain, and H.-J. Zhang. Image classification for content-based indexing. IEEE Trans. on Image Processing, 10(1):117—130, Jan. 2001.
[92] A. Vailaya, A. K. Jain, and H.-J. Zhang. On image classification: City images vs. landscapes. Pattern Recognition, 31(10):1921—1935, 1998.
[93] J. Vermaak, P. Perez, and M. Gangnet. Rapid summarization and browsing of video sequences. In Proc. of the British Machine Vision Conf., pages 145—151, 2002.
[94] W. A. Wagenaar. My memory: A study of autobiographical memory over six years. In Cognitive Psychology, pages 225—252, 1986.
[95] X.-J. Wang, W.-Y. Ma, Q.-C. He, and X. Li. Grouping web image search result. In Proc. of the ACM Int’l Conf. on Multimedia, pages 436—439, 2004.
[96] Z. Yang and C.-C. Jay Kuo. Survey on Image Content Analysis, Indexing, and Retrieval Techniques and Status Report of MPEG-7. Tamkang Journal of Science and
Engineering, 2(3):101—118, 1999.
[97] P. N. Yianilos. Data strucutures and algorithms for nearest neighbor search in general metric spaces. In Proc. of the Ann. Symp. Discrete Algorithms, volume 1, pages 516—
523, 1993.
[98] J. Yu and Q. Tian. Toward intelligent use of semantic information on subspace discovery for image retrieval. In Proc. of the IEEE Int’l Conf. on Multimedia and Expo, pages 293—296, 2006.
[99] L. Zhang, L. Chen, M.-J. Li, and H.-J. Zhang. Automated annotation of human faces in family albums. In Proc. of the ACM Int’l Conf. on Multimedia, pages 355—358, 2003.
[100] L. Zhang, Y. Hu,M.-J. Li,W.-Y. Ma, and H.-J. Zhang. Efficient propagation for face annotation in family albums. In Proc. of the ACM Int’l Conf. on Multimedia, pages 716—723, 2004.