跳到主要內容

簡易檢索 / 詳目顯示

回結果列表
研究生： 王宇晨
Yu-Chen Wang
論文名稱： 以特徵線為基礎之子空間學習方法及其應用
Nearest Feature-Line Embedding Subspace Learning Based Method and Its Applications
指導教授： 范國清
Kuo-Chin Fan
口試委員:
學位類別： 博士
Doctor
系所名稱： 資訊電機學院 - 資訊工程學系
Department of Computer Science & Information Engineering
論文出版年： 2015
畢業學年度： 103
語文別： 中文
論文頁數： 109
中文關鍵詞： 流形學習 、區域性 、多媒體應用 、車輛顏色辨識 、相關性回饋影像檢索系統
外文關鍵詞： manifold learning, locally, multimedia application, vehicle color classification, relevance feedback image retrieval system
相關次數： 點閱：9  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 近年來，流形學習(manifold learning)的技術被應用到許多圖形識別的領域中，流形學習的方法就是基於所要分析的物件資料在高維度空間中有平滑流形分佈的假設，再利用轉置（re-embed）的方法將物件資料投影到較低維度的歐氏空間，並區域性（locally）保持其原有在流形上的分佈。由於多媒體應用的蓬勃發展，常需要分析如視訊、聲音、高解析度影像等資料，而這些多媒體資料其特徵向量往往都是採用高維度的方式來描述，因此需要一適當的降維方法使多媒體資料在降維後仍然可以保持其在原始高維特徵空間中的結構關係，而流形學習正是符合這樣概念的方法。在本研究中，我們提出了一個新的最近特徵線子空間學習方法，並且將其應用於車輛顏色辨識及相關性回饋影像檢索系統。透過這樣的子空間學習方法可以保留樣本區域結構拓樸及達到最大邊際投影的效果。在實驗結果中，我們的演算法與其他著名的演算法在比較後都有較佳的辨識結果。

    In recent years, manifold learning has attracted a lot of researchers. Manifold learning assumes data set in the high-dimensional feature space are with specific manifold distribution, which could be re-embed into the low-dimensional Euclidean space. The rapid generation on digital information has made the development of efficient multimedia applications become urgently. Abundant multimedia data (video, audio and high-resolution image) need intelligent analysis which can be become useful information for multimedia application, such as video surveillance, audio classification and image retrieval. In addition, these multimedia data are often characterized by integrating high-dimensional features. Therefore, an approach which can effectively reduce high-dimensional features and keep its structural relationship in the low-dimensional feature space is required. As mentioned above, the manifold learning method is consistent with this concept. In this study, we proposed a novel nearest feature-line subspace learning method, and this method is applied to vehicle color classification and relevance feedback image retrieval system. According to our proposed subspace learning method which can be effectively preserved structural locality of samples and maximum margin projection in the new feature space. Experimental results have shown that our proposed method outperformed several state-of-the-art algorithms.

    Content 摘要 V Abstract VI 誌謝 VII Chapter 1：Introduction 1 1.1 Motivation 1 1.2 Contributions of This Presentations 6 1.3 Organization of The Dissertation 8 Chapter 2：Review of Related Works 9 2.1 Previous Methods for Vehicle Color Classification 9 2.2 Previous Methods for Manifold Learning Method 11 2.3 A Review of Eigenspace Approach in Relevance Feedback Image Retrieval 15 Chapter 3：Vehicle Color Classification Using Nearest Feature Line Embedding 22 3.1 Location of Region of Interest(ROI) 23 3.1.1 Red Patch Labeling 24 3.1.2 Taillight Pair Matching 26 3.1.3 Type Classification Using Shape Feature 27 3.2 Eigenspace-based Color Classification 31 3.2.1 Feature Representation: Linear Color Feature Combination Classification 32 3.2.2 Feature Discriminant Analysis: Dimension Reduction 33 3.2.3 Classifier Design: 1-NN, SVM, and SRC 39 Chapter4：Content-Based Image Retrieval Using Biased Discriminant Analysis with Feature-Line Embedding 41 4.1 Evaluation of Nearest Feature Space Embedding 41 4.2 Proposed Biased Discriminant Analysis Method 43 4.3 Optimal Linear Embedding 44 4.4 Comparison of FLE-BDA and the Two-Class NFL Classifier 46 4.5 CBIR Using Biased Discriminant Analysis with Feature Line Embedding 47 Chapter5：Experimental Results 50 5.1 Evaluation of Eigenspace-Based in Vehicle Color Classification 50 5.1.1 ROI Location 53 5.1.2 Vehicle Color Classification 59 5.2 Evaluation of Eigenspace-Based in Relevance Feedback Image Retrieval 68 5.2.1 The Toy Example 69 5.2.2 Result of Image dataset COREL-10K 75 5.2.3 Results on Image Dataset SIMPLIcity 80 5.2.4 Results on 3D Dataset SHREC-W 84 5.2.5 Results on Image Dataset CIFAR-10 87 5.2.6 Results on Image Dataset NUS-WIDE 89 Chapter 6：Conclusions 91 References 92

    References
    [1] T. Deselaers, D. Keysers and H. Ney, “Features for image retrieval: An experimental comparison,” Information Retrieval, vol. 11, pp. 77-107, 2008.
    [2] A. Frome, Y. Singer and J. Malik, “Image retrieval and classification using local distance functions,” Advances in Neural Information Processing Systems, vol. 19, pp. 417-424, 2007.
    [3] J. Zhang and L. Ye, “Local aggregation function learning based on support vector machines,” Signal Processing, vol. 89, pp. 2291-2295, 2009.
    [4] P. S. Hiremath and J. Pujari, “Content based image retrieval model using color, texture and shape features,” International Conference on Advanced Computing and Communications, pp.780-784, 2007.
    [5] Y. D. Chun, N. C. Kim and I. H. Jang, “Content-based image retrieval using multi-resolution color and texture features,” IEEE Transactions on Multimedia, vol. 10, pp. 1073-1084, 2008.
    [6] G. H. Liua, Z. Y. Li, L. Zhang and Y. Xu, “Image retrieval based on micro-structure descriptor,” Pattern Recognition, vol. 44, no. 9, pp. 2123-2133, 2011.
    [7] J. Yua, Z. C. Qin, T. Wan and X. Zhang, “Feature integration analysis of bag-of-features model for image retrieval,” Neurocomputing, vol. 120, no. 23, pp. 355-364, 2013.
    [8] Y. Wu and A. Zhang. “A feature re-weighting approach for relevance feedback in image retrieval,” IEEE International Conference on Image Processing, pp. 581-584, 2002.
    [9] Y. Rui, T. Huang, and S. Mehrotra, “Content-based image retrieval with relevance feedback in MARS,” IEEE International Conference on Image Processing, pp. 815-818, 1997.
    [10] X. Zhou and T. Huang, “Small sample learning during multimedia retrieval using BiasMap,” IEEE International Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 11-17, 2001.
    [11] N. Baek, S. M. Park, K. J. Kim, and S. B. Park, “Vehicle color classification based on the support vector machine method,” Proceedings of Communications in Computer and Information Science, vol. 2, pp. 1133-1139, 2007.
    [12] K. J. Kim, S. M. Park and Y. J. Choi, “Deciding the number of color histogram bins for vehicle color recognition,” Proceedings of IEEE Asia-Pacific Services Computing Conference, pp. 134-138, 2007.
    [13] M. J. Yang, G. Han, X. F. Li, X. C. Zhu and L. Li, “Vehicle color recognition using monocular camera,” Proceedings of IEEE International Conference on Wireless Communications and Signal Processing, pp. 1-5, 2011.
    [14] L. W. Tsai, J. W. Hsieh and K. C. Fan, “Vehicle detection using normalized color and edge map,” IEEE Transactions on Image Processing, vol. 16, pp.850-864, 2007.
    [15] S. Y. Chen, J.W. Hsieh, J. C. Wu and Y. S. Chen, “Vehicle retrieval using eigen color and multiple instance learning,” International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp.657-660, 2009.
    [16] L. M. Brown, “Example-based color vehicle retrieval for surveillance,” IEEE International Conference on Advanced Video and Signal Based Surveillance, pp.91-96, 2010.
    [17] X. Li, G. Zhang, J. Fang, J. Wu, and Z. Cui, “Vehicle color recognition using vector matching of template,” Proceedings of the 2010 International Symposium on Electronic Commerce and Security, pp.189-193, 2010.
    [18] G. D. Finlayson, B. Schiele, and J. L. Crowley, “Comprehensive color image normalization,” Proceedings of 5th European Conference on Computer Vision, vol. 1, pp. 475-490, June 1998.
    [19] J. W. Hsieh, L. C. Chen, S. Y. Chen, S. C. Lin, D. Y. Chen, “Vehicle color classification under different lighting conditions through color correction,” IEEE International Symposium on Circuits and Systems, pp. 1859-1862, 2012.
    [20] Y. Shen, R. Mo, Y. Zhu, L. Wei, W. Gao and Z. Peng, “Over-exposure image correction with automatic texture synthesis,” Proceedings of International Congress on Image and Signal Processing, pp. 794-797, 2011.
    [21] D. Guo, Y. Cheng, S. Zhuo and T. Sim, “Correcting over-exposure in photographs,” Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 515-521, 2010.
    [22] H. Stokman and T. Gevers, “Selection and fusion of color models for image feature detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 3, pp. 371-381, 2007.
    [23] P. Wolfe, “The simplex method for quadratic programming,” Econometrica, vol. 27, no. 3, pp. 382-398, 1959.
    [24] S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science, vol. 290, no. 22, pp. 2323-2326, 2000.
    [25] X. He, S. Yan, Y. Ho, P. Niyogi, and H. J. Zhang, “ Face recognition using Laplacianfaces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328-340, 2005.
    [26] Y. N. Chen, C. C. Han, C. T. Wang, and K. C. Fan, “ Face recognition using nearest feature space embedding,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 6, pp. 1073-1086, 2011.
    [27] J. Tenenbaum, V. de Silva, and J. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science, vol. 290, no. 5500, pp. 2319-2323, 2000.
    [28] M. Belkin and P. Niyogi, “Laplacian eigenmaps and spectral techniques for embedding and clustering,” Advances in Neural Information Processing Systems, vol. 14, pp. 585-591, MIT Press, 2001.
    [29] B. Richard, “Adaptive control processes: a guided tour,” Princeton University Press, Wiley, pp. 143-397, 1961.
    [30] H. Hotelling. “Analysis of a complex of statistical variables into principal components,” Journal of Educational Psychology, vol. 24, pp. 417-441, 1933.
    [31] R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Annals of Eugenics, vol. 7, pp. 179-188, 1936.
    [32] P. Belhumeur, J. Hespanha, and D. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, 1997.
    [33] S. Yan, D. Xu, B. Zhang, H. J. Zhang, Q. Yang, and S. Lin, “Graph embedding and extensions: General framework for dimensionality reduction,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 40-51, 2007.
    [34] D. Tao, X. Tang, X. Li, and Y. Rui, “Kernel direct biased discriminant analysis: A new content-based image retrieval relevance feedback algorithm,” IEEE Transactions on Multimedia, vol. 8, no. 4, pp. 716-724, 2006.
    [35] D. Xu, S. Yan, D. Tao, S. Lin, and H. Zhang, “Marginal Fisher Analysis and its variants for human gait recognition and content-based image retrieval,” IEEE Transactions on Image Processing, vol. 16, no. 11, pp. 2811-2821, 2007.
    [36] X. F. He, D. Cai and J. W. Han, “Learning a maximum margin subspace for image retrieval,” IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 2, pp. 189-201, 2008.
    [37] L. Zhang, L. Wang and W. Lin, “Semi-supervised biased maximum margin analysis for interactive image retrieval,” IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 2294-2308, 2012.
    [38] Y. Y. Lin, T. L. Liu and H. T. Chen, “Semantic manifold learning for image retrieval,” ACM International Conference on Multimedia, pp. 249-258, 2005.
    [39] W. Bian and D. Tao, “Biased discriminant Euclidean embedding for content-based image retrieval,” IEEE Transactions on Image Processing, vol. 19, no. 2, pp. 545-554, 2010.
    [40] E. Dule, M. Gokmen and M. S. Beratoglu, “A convenient feature vector construction for vehicle color recognition,” Proceedings of 11th WSEAS International Conference on Neural Networks, Evolutionary Computing and Fuzzy systems, pp. 250-255, 2010.
    [41] Y. T. Wu, J. H. Kao, and M. Y. Shih, “A vehicle color classification method for video surveillance system concerning model-based background subtraction,” Proceedings of Pacific-Rim Conference on Multimedia, vol. 6297, pp. 369-380, 2010.
    [42] Y. Y. Lu, C. C. Han, M. C. Lu and K. C. Fan, “A vision-based system for the prevention of car collisions at night,” Machine Vision and Applications, vol. 22, pp. 117-127, 2011.
    [43] R.-E. Fan, P.-H. Chen, and C.-J. Lin, “Working set selection using second order information for training SVM,” Journal of Machine Learning Research, vol. 6, pp. 1889-1918, 2005.
    [44] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” Proceeding of IEEE International Conference on Computer Vision and pattern Recognition, vol. 1, pp. 886-893, 2005.
    [45] G. Wyszecki and W. S. Stiles, Color science: Concepts and methods, quantitative data and formulae, John Wiley and Sons, second edition, 1982.
    [46] S. Z. Li, “Face recognition based on nearest linear combinations,” Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 839-844, 1998.
    [47] J. Wright, Allen Y. Yang, A. Ganesh, S. S. Sastry and Y. Ma, “Robust face recognition via sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210-227, 2009.
    [48] S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004.
    [49] E. Candes and J. Romberg, -magic: Recovery of sparse signals via convex programming, http://www.acm.caltech.edu/l1magic/, 2005.
    [50] J. Z. Wang, J. Li and G. Wiederhold, “SIMPLIcity: Semantics-sensitive integrated matching for picture libraries,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 947-963, 2001.
    [51] G. H. Liu and J. Y. Yang, “Content-based image retrieval using color difference histogram,” Pattern Recognition, vol. 46, no. 1, pp. 188-198, 2013.
    [52] R. C. Veltkamp and F.B. ter Haar, “SHREC 2007: 3D shape retrieval contest,” Technical Report UUCS-2007-015, pp. 5-10, 2007.
    [53] D. P. Huijsmans and N. Sebe, “How to complete performance graphs in content-based image retrieval: Add generality and normalize scope,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 245-251, 2005.
    [54] J. Ricard, D. Coeurjolly and A. Baskurt, “ART extension for description, indexing and retrieval of 3D objects,” Pattern Recognition, vol. 3, pp.79-82, 2004.
    [55] J. L. Shih, C. H. Lee and J. T. Wang, “3D object retrieval system based on grid D2,” Electronics Letters, vol. 41, no. 4, pp. 23-24, 2005.
    [56] J. L. Shih, and H. Y. Chen, “3D model retrieval based on grid sphere and dodecahedral silhouette descriptors,” Proceeding of Joint Conference on Information Science, 2006.
    [57] J. L. Shih, C. H. Lee, and J. T. Wang, “A new 3D model retrieval approach based on the elevation descriptor,” Pattern Recognition, vol. 40, no. 1, pp. 283-295, 2007.
    [58] J. L. Shih and W. C. Wang, “A 3D model retrieval approach based on the principal plane descriptor,” Proceedings of Innovative Computing, Information and Control, 2007.
    [59] A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Master’s thesis, Department of Computer Science, University of Toronto, 2009.
    [60] T. S. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y. T. Zheng, “NUS-WIDE: A Real-World Web Image Database from National University of Singapore,” ACM International Conference on Image and Video Retrieval. Greece. Jul. 8-10, 2009.
    [61] 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, vol. 30, no. 11, pp. 1958-1970, 2008.
    [62] W. Liu, J. Wang, S. Kumar, and S. F. Chang, “Hashing with graphs,” International Conference on Machine Learning, pp. 1-8, 2011.
    [63] J. Masci, M. Bronstein, A. Bronstein, and J. Schmidhuber, “Multimodal similarity-preserving hashing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 4, pp. 824-830, 2014.

    QR CODE
    :::