A Meta-Feature Representation Approach toImage Annotation

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：18

、訪客IP：3.146.152.99

姓名

廖雅蓮(Ya-Lien Liao) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

(A Meta-Feature Representation Approach toImage Annotation)

相關論文

★ 利用資料探勘技術建立商用複合機銷售預測模型	★ 應用資料探勘技術於資源配置預測之研究-以某電腦代工支援單位為例
★ 資料探勘技術應用於航空業航班延誤分析-以C公司為例	★ 全球供應鏈下新產品的安全控管-以C公司為例
★ 資料探勘應用於半導體雷射產業-以A公司為例	★ 應用資料探勘技術於空運出口貨物存倉時間預測-以A公司為例
★ 使用資料探勘分類技術優化YouBike運補作業	★ 特徵屬性篩選對於不同資料類型之影響
★ 資料探勘應用於B2B網路型態之企業官網研究-以T公司為例	★ 衍生性金融商品之客戶投資分析與建議-整合分群與關聯法則技術
★ 應用卷積式神經網路建立肝臟超音波影像輔助判別模型	★ 基於卷積神經網路之身分識別系統
★ 能源管理系統電能補值方法誤差率比較分析	★ 企業員工情感分析與管理系統之研發
★ 資料淨化於類別不平衡問題: 機器學習觀點	★ 資料探勘技術應用於旅客自助報到之分析—以C航空公司為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

隨著電腦網路與資訊科技蓬勃發展，影像自動命名與檢索的議題應運而生，如何提供使用者準確及有效率的影像檢索成為探索的目標。近年來以影像內容特徵為基礎的影像檢索方法蓬勃發展。然而，直接使用影像原始低階特徵的分類準確率偏低，造成影像經常被命名了不恰當的關鍵字。本論文提出一種新的特徵表示法達成優於使用原始低階特徵的分類準確率，稱為Meta-Features 表示法。本論文使用Corel 資料集來驗證Meta-features 能夠達成較好的分類成效。為了充分驗證，實驗採用三種知名的分類器來進行比較，分別是Support Vector Machines (SVM)、k-Nearest Neighbor (k-NN)與naïve Bayes 分類器。

摘要(英)

This thesis proposes a feature representation approach namely meta-feature representation for automatic image annotation. Automatic image annotation technology aims to provide an efficient and effective searching environment for users to query images by keywords, which solves the limitation of Content-Based Image Retrieval (CBIR) that low-level image features are only extracted and used for similarity search. It is the fact that low-level image features do not directly correspond to high-level concepts of users. This causes many incorrect keyword assignments to images since a certain number of semantically similarn images have dissimilar low-level features and semantically dissimilar images have similar low-level features. The
meta-features proposed in this thesis are extracted from the original low-level image features by nine transformation formulas to improve annotation accuracy. We use the Corel dataset for the experiments to show the performance improvement of image annotation based on the meta-features. In particular, for classifier design, this thesis considers three well-known and popular classifiers for image annotation. They are the k-Nearest Neighbor (k-NN) classifier, Support Vector Machines (SVM), and the naïve Bayes classifier.

關鍵字(中)

★ 圖片特徵表示法
★ 影像檢索
★ 圖片命名
★ 圖片分類

關鍵字(英)

★ feature representation
★ image retrieval
★ image annotation
★ image classification

論文目次

摘要 i
ABSTRACT ii
致謝辭 iii
LIST OF FIGURES vii
LIST OF TABLES x
CHAPTER1 INTRODUCTION 1
1.1 Background 1
1.2 Motivation 2
1.3 Research Objectives 5
1.4 Organization of the Thesis 6
CHAPTER2 LITERATURE REVIEW 7
2.1 Image Annotation 7
2.2 Feature Extraction and Representation 8
2.2.1 Global and Local Features 8
2.2.2 Segmentation 9
2.2.3 Feature Representation 10
2.3 Machine Learning Techniques 12
2.3.1 Supervised and Unsupervised Learning Techniques 12
2.3.2 K-Nearest Neighbor 13
2.3.3 Support Vector Machines 14
2.3.4 Naive Bayes 16
2.4 Related Work 17
2.4.1 Low-Level Feature Representations 17
2.4.2 Latent Semantic Indexing 18
2.4.3 Contextual Features 19
2.4.4 Discussions 20
CHAPTER3 META-FEATURE REPRESENTATION 21
3.1 The process of our method 21
3.1.1 Extraction of Cluster Centers 21
3.2 The Meta-Features 23
3.2.1 MF1 23
3.2.2 MF2 24
3.2.3 MF3 24
3.2.4 MF4 25
3.2.5 MF5 25
3.2.6 MF6 26
3.2.7 MF7 26
3.2.8 MF8 27
3.2.9 MF9 27
3.3 Discussions 28
CHAPTER4 EXPERIMENT 29
4.1 Experimental Setup 29
4.1.1 Experiment Process 29
4.1.1 The Corel Dataset 30
4.1.2 Image Segmentation 34
4.1.3 Low-Level Feature Extraction 34
4.1.4 Classifier Design 35
4.1.5 Evaluation Methods 36
4.2 Pre-test Analysis 37
4.2.1 Principal components Analysis 37
4.2.2 Distances between Images 39
4.3 Study I: Local Region-based Image Annotation 41
4.3.1 Concrete Keywords 42
4.3.2 Abstract Keywords 50
4.4 Study II: Global based Image Annotation 60
4.5 Further Comparisons 66
CHAPTER5 CONCLUSION AND FUTURE WORK 71
5.1 Conclusion 71
5.2 Future Work 72
REFERENCES 73
APPENDIX A. EXPERIMENTAL RESULTS OF PCA 79

參考文獻

[1]. Antani, S., Kasturi, R., and Jain, R. (2002) A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video. Pattern Recognition, vol. 35, pp. 945-965.
[2]. Asconcelos, N. (2007). From pixels to semantic spaces: Advances in content-based image retrieval. Computer, 40(7), pp.20-26, UC San Diego.
[3]. Aslandogan, Y.A. and Yu, C.T. (1999) Techniques and systems for image and video retrieval. IEEE Transactions on Knowledge and Data Engineering, vol. 11, no. 1, pp.56-63.
[4]. Awcock, G.W. and Thomas, R. (1996) Applied image processing. McGraw-Hill, New York.
[5]. Barnard, K., Duygulu, P., Forsyth, D., de Freitas, N., Blei, D., and Jordan, M.I. (2003a) Matching words and pictures. Journal of Machine Learning Research, vol. 3, pp. 1107-1135.
[6]. Barnard, K., Duygulu, P., Guru, R., Gabbur, P., and Forsyth, D. (2003b) The effects of segmentation and feature choice in a translation model of object recognition. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, June 16-22, pp. 675-682.
[7]. Blei, D.M. and Jordan, M.I. (2003) Modeling annotated data. Proceedings of the 26th International ACM SIGIR Conference on Research and Development in Information Retrieval, Toronto, Canada, July 28-Aug. 1, pp. 127-134.
[8]. Caelli, T. and Bischof, W.F. (1997) The role of machine learning in building image interpretation systems. International Journal of Pattern Recognition and Artificial Intelligence, vol. 11, no. 1, pp. 143-168.
[9]. Castleman, K.R. (1996) Digital Image Processing. Prentice-Hall, New Jersey.
[10]. Cawkell, A.E. (1992) Selected aspects of image processing and management: review and future prospects. Journal of Information Science, vol. 18, no. 3, pp. 179-192.
[11]. Chang, C.C. and Lin, C.J., LIBSVM: a library for support vector machines, vol. 80: 604–611, 2001.
[12]. Choi, Y. and Rasmussen, E.M. (2002) Users’ relevance criteria in image retrieval in American history. Information Processing & Management, vol. 38, no. 5, pp. 695-726.
[13]. Cover, T. and Hart, P. (1967) Nearest neighbor pattern classification. IEEE Transactions on Information Theory, vol.13, no.1, pp. 21- 27.
[14]. Das, G. and Ray, S. (2006) Effect of Finite Sample Size in Content-Based Image Retrieval. Video and Signal Based Surveillance. AVSS '06. IEEE International Conference on, vol., no., pp.96-96, Nov.
[15]. Datta, R., Joshi, D., Li, J., and Wang, J.Z. (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Computing Surveys, vol. 40, no. 2, article 5.
[16]. Daubechies, I. (1992) Ten lectures on wavelets. Society for Industrial and Applied Mathematics, Philadelphia.
[17]. Daugman, J.G. (1990) An information-theoretic view of analog representation in striate cortex. In Computational Neuroscience (pp.403-423), Schwartz, E.L. (Ed.), MIT Press, Massachusetts.
[18]. Deerwester, S., Dumais, S.T.; Furnas, G.W., Landauer, T.K. and Harshman, R. (1990) Indexing by latent semantic indexing. Journal of the American Society for Information Science, vol.41, no. 6.
[19]. Deselaers, T., Deserno, T.M., and Muller, H. (2008) Automatic medical image annotation in ImageCLEF 2007: overview, results, and discussion. Pattern Recognition Letters, vol. 29, pp. 1988-1995.
[20]. Duda, R.O., Hart, P.E., and Stork, D.G. (2001) Pattern Classification (2nd Edition). John Wiley, New York.
[21]. Eakins, J.P., Briggs, P., and Burford, B. (2004) Image retrieval interfaces: a user perspective. Proceedings of the International Conference on Image and Video Retrieval, Dublin, Ireland, July 21-23, pp. 628-637.
[22]. Givers, T. and Smeulders, A. W. M. (1996) A comparative study of several color models for color image invariant retrieval, Proceedings of the First International Workshop on Image Databases and Multi Media search, Amsterdam, The Netherlands, August 22-23, pp. 17-26.
[23]. Goodrum, A. and Spink, A. (2001) Image searching on the Excite Web search engine. Information Processing & Management, vol. 37, no. 2, pp. 295-311.
[24]. Grigorescu, S.E., Petkov, N., and Kruizinga, P. (2002) Comparison of texture features based on Gabor filters. IEEE Transactions on Image Processing, vol. 11, no. 10, pp. 1160-1167.
[25]. Gupta, A., Santini, S. and Jain, R. (1997) In search of information in visual media. Commun ACM ,40(12):35–42.
[26]. Keefe, J.M. (1990) The image as document: descriptive programs at Rensselaer. Library Trends, vol. 38, no. 4, pp. 659-681.
[27]. Idris, F. and Panchanathan, S. (1997) Review of image and video indexing techniques. Journal of Visual Communication and Image Representation, vol. 8, no. 2, pp. 146-166.
[28]. Ito, H., Kawai ,Y. and Koshimizu, H. (2008) Face Image Annotation Based on Latent Semantic Space and Rules. 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, pp.766-773.
[29]. Jahne, B. (1995) Digital Image Processing: Concepts, Algorithms, and Scientific Applications. Springer-Verlag, Berlin.
[30]. Jeong, J., Hwang, C. and Jeon, B. (2009, January) An efficient method of image identification by combining image features. International Conference on Ubiquitous Information Management and Communication, Suwon, Korea.
[31]. Jeon, J., Lavrenko, V., and Manmatha, R. (2003, July) Automatic image annotation and retrieval using cross-media relevance models. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and development in information retrieval, Toronto, Canada, pp. 119-126.
[32]. Wang, J.Z., Li, J. and Wiederhold, G. (2001) SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Analysis and Machine Intelligence, 23(9), 947–963.
[33]. Li, J. and Wang, J.Z. (2003) Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1075-1088.
[34]. Livens, S., Scheunders, P., Van de Wouwer, G. and Van Dyck, D. (1997) Wavelet for texture analysis: an overview. Proceedings of the IEEE International Conference on Image Processing and its Applications, Dublin, Ireland, July 14-17, p. 581-585.
[35]. Long, F., Zhang, H., and Feng, D.D. (2003) Fundamentals of content-based image retrieval. In Multimedia Information Retrieval and Management – Technological Fundamentals and Applications. Feng, D.D., Siu, W.C., Zhang, H. (Eds.), Springer-Verlag, Germany.
[36]. Manning , C.D. , Raghavan, P. , Schütze, H. (2008) Introduction to Information Retrieval. Cambridge University Press. ISBN: 0521865719.
[37]. Markkular, M, Tico, M., Sepponen, B., Nirkkonen, K. and Sormunen, E. (2001) A test collection for the evaluation of content-based image retrieval algorithms – a user and task-based approach. Information Retrieval, vol. 4, no. 3-4, pp. 275-293.
[38]. Mathias, E. (1998) Comparing the influence of color spaces and metrics in content-based image retrieval. Proceedings of the IEEE International Symposium on Computer Graphics, Image Processing, and Vision, Rio de Janeiro, Brazil, Oct. 20-23, pp. 371-378.
[39]. Mitchell, T. (1997) Machine Learning. McGraw-Hill, New York.
[40]. Mylonas , P., Spyrou , E., Avrithis ,Y. and Kollias,S. (2009) Using visual context and region semantics for high-level concept detection. IEEE Transactions on Multimedia, vol. 11, NO. 2.
[41]. Papathomas, T.V., Conway, T.E., Cox, I.J., Ghosn, J., Miller, M.L., Minka, T.P., and Yianilos, P.N. (1998, January) Psychophysical studies of the performance of an image database retrieval system. Proceedings of the SPIE Conference on Human Vision and Electronic Imaging III, vol. 3299, San Jose, California, 24-30, pp. 591-602.
[42]. Paredes, R., Pérez, J. C., Juan, A., and Vidal, E. (2001, July) Local representations and a direct voting scheme for face recognition. In In Proc. of the Workshop on Pattern Recognition in Information Systems.
[43]. Rish, Irina. (2001). An empirical study of the naive bayes classifier. IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence.
[44]. Rui, Y., Huang, T. S., and Chang, S. F. (1999) Image retrieval: current techniques, promising directions and open issues. Journal of Visual Communication and Image Representation, vol. 10, no. 1, pp. 39-62.
[45]. Sebe, N. and Lew, M.S. (2001) Texture feature for content-based retrieval. In Principles of Visual Information Retrieval, Lew, M. S. (Ed.), Springer-Verlag, London.
[46]. Shi, J. and Malik, J. (2000) Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22 No. 8, pp. 888-905.
[47]. Smith, L. I. (2002, February) A tutorial on principal component analysis. http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf
[48]. Tang, J., Lewis, P.H. (2007, March) A Study of Quality Issues for Image Auto-Annotation with the Corel Data-Set. IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, no.3.
[49]. Tsai, C. F., McGarry, K., and Tait, J. (2006a) Qualitative evaluation of automatic assignment of keywords to images. Information Processing & Management, vol. 42, no. 1, pp. 136-154.
[50]. Tsai, C. F., McGarry, K., and Tait, J. (2006b) CLAIRE: a modular support vector image indexing and classification system. ACM Transactions on Information Systems, vol. 24, no. 3, pp. 353-379.
[51]. Tsai, C. F. and Hung, C. (2008) Automatically annotating images with keywords: a review of image annotation systems. Recent Patents on Computer Science. vol. 1, no. 1, pp. 55-68.
[52]. Tsang, I. W., Kwok , J.T., Cheung, P. M. (2005) Core vector machines: Fast SVM training on very large data sets. The Journal of Machine Learning Research, vol. 6, p.363-392.
[53]. Tuceryan, M. and Jain, A.K. (1998) Texture analysis. In The Handbook of Pattern Recognition and Computer Vision, 2nd Edition. Chen, C.H., Pau, L.F., and Wang, P. S. P. (Eds.), World Scientific, Singapore.
[54]. Venters, C. C. and Cooper, M. (2000, June) A Review of Content-Based Image Retrieval Systems. JISC Technology Application Program. Technical Report JTAP-054.
[55]. Wong, W.T. and Hsu, S.H. (2006) Application of SVM and ANN for image retrieval. European Journal of Operational Research, vol. 173, pp. 938-50.
[56]. Wang,Y., Mei,T., Gong,S. and Hua X.S. (2009) Combining global, regional and contextual features for automatic image annotation. Pattern Recognition,vol. 42, 259 – 266.
[57]. Wu, J.K., Kankanhalli, M.S., Lim, J.-H., and Hong, D. (2000) Perspectives on content-based multimedia systems. Kluwer Academic Publishers, Massachusetts.
[58]. Wu, L., Hu,Y., Li, M., Yu, N., Hua, X.S. (2009) Scale-invariant visual language modeling for object categorization. IEEE Transactions on Multimedia, vol. 11, no. 2.
[59]. Wu, X. and Kumar, V. (2009) Top ten algorithms in data mining. Knowledge and Information Systems, vol. 14 (1), pp. 1–37.

指導教授

蔡志豐(Chih-Fong Tsai)

審核日期

2010-7-16

推文