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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 楊佳玲 | |
| dc.contributor.author | Ming-Wei Hung | en |
| dc.contributor.author | 洪銘蔚 | zh_TW |
| dc.date.accessioned | 2021-06-13T01:03:40Z | - |
| dc.date.available | 2007-07-26 | |
| dc.date.copyright | 2007-07-26 | |
| dc.date.issued | 2007 | |
| dc.date.submitted | 2007-07-24 | |
| dc.identifier.citation | [1] M. Bilenko, S. Basu, and R. J. Mooney, “Integrating Constraints and Metric Learning in Semi-Supervised Clustering”, in Proceedings of ICML, 2004.
[2] D. Blei and M. Jordan, “Modeling Annotated Data”, in Proceedings of ACM SIGIR, 2003 [3] G. Carneiro and N. Vasconcelos, “Formulating Semantic Image Annotation as a Supervised Learning Problem”, in Proceedings of CVPR, 2005. [4] E. Y. Chang, K. Goh, G. Sychay, and G. Wu, “CBSA: Content-based Soft Annotation for Multimodal Image Retrieval Using Bayes Point Machines”, IEEE Transaction on Circuits and Systems for Video Technology, 13(1):26–38, 2003. [5] I. Cohen, F. G. Cozman, N. Sebe, M. C. Cirelo, and T. S. Huang, “Semisupervised Learning of Classifiers: Theory, Algorithms, and Their Application to Human-Computer Interaction”, IEEE Transaction on PAMI, Vol. 26, No. 12, Dec. 2004. [6] R. Datta, J. Li, and J. Z. Wang, “Content-Based Image Retrieval - Approaches and Trends of the New Age”, in Proceedings of the ACM SIGMM international workshop on Multimedia information retrieval, Singapore, Nov., 2005 [7] P. Duygulu, K. Barnard, J. F. G. de Freitas, and D. A. Forsyth, “Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary”, in Proceedings of European Conference on Computer Vision, pages 97-112, 2002. [8] S. L. Feng, R. Manmatha, and V. Lavrenko, “Multiple Bernoulli Relevance Models for Image and Video Annotation”, in Proceedings of CVPR, 2004. [9] R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman, “Learning object categories from Google’s image search”, in Proceedings of ICCV, 2005. [10] T. Hofmann, “Probabilistic latent semantic indexing”, in proceedings of ACM SIGIR, 1999. [11] J. Jeon, V. Lavrenko, and R. Manmatha, “Automatic Image Annotation and Retrieval using Cross-Media Relevance Models”, in Proceedings of ACM SIGIR, 2003. [12] W. Jin, R. Shi, and T. -S. Chua, “A Semi-Naïve Bayesian Method Incorporating Clustering with Pair-Wise Constraints for Auto Image Annotation”, in Proceedings of ACM MM, 2004. [13] V. Lavrenko and W. Croft, “Relevance-Based Language Models”, in proceedings of ACM SIGIR, pp. 120-127, 2001. [14] D. Lowe, “Object recognition from local scale-invariant features”, In Proceedings of the 7th International Conference on Computer Vision, Kerkyra, Greece, pages 1150-1157, September 1999. [15] D. Lowe, “Distinctive image features from scale-invariant keypoints”, International Journal of Computer Vision, 60(2):91-110, 2004. [16] X. Qian, X. Du, and Q. Wang, “Semi-Supervised Hierarchical Clustering Analysis for High Dimensional Data”, International Journal of Information Technology, Vol.12, No.3, 2006. [17] J. Sivic, B. C. Russell, A. A. Efros, A. Zisserman, and W. T. Freeman, “Discovering objects and their location in images”, in Proceedings of ICCV, 2005. [18] M. Srikanth, J. Varner, M. Bowden, and D. Moldovan, “Exploiting Ontologies for Automatic Image Annotation”, in Proceedings of ACM SIGIR, 2005. [19] L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations”, IEEE Trans. on Pattern Analysis and Machine Intelligence, June 1991, Vol. 13(6), 583-598. [20] D. Wang, “A multiscale gradient algorithm for image segmentation using watersheds”, Pattern Recognition, 30(12), 2043-2052, 1997. [21] L. Xu and D. Schuurmans, “Unsupervised and Semi-Supervised Multi-Class Support Vector Machines”, in Proceedings of the Twentieth National Conference on Artificial Intelligence, 2005. [22] D. Yarowsky, “Unsupervised Word Sense Disambiguation Rivaling Supervised Methods”, in Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pp. 189–196, 1995. [23] H. J. Zhang, W. Y. Liu, and C. H. Hu, “iFind - A System for Semantics and Feature Based Image Retrieval over Internet”, in Proceedings of ACM MM, pp.477-478, Oct. 2000. [24] X. D. Zhou, L. Chen, J. Ye, Q. Zhang, and B. Shi, “Automatic Image Semantic Annotation Based on Image-Keyword Document Model”, in Proceedings of CIVR, 2005. [25] X. Zhu, “Semi-Supervised Learning with Graphs”, PhD Thesis, CMU, 2005. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/29270 | - |
| dc.description.abstract | Retrieving images by textual queries requires some knowledge of the semantics of the image. Hence we need to find the label words that describe the content of the image and take them as the annotation of the image. Here, we propose an approach to annotate images with user feedback, and it annotates a label a time. The process contains a loop, and it will report a number of images which are most likely to be associated with the label word for user to annotate every iteration. The way to estimate the possibility that an image is associated with a label is using the known labeled images and some unlabeled images as training data to train a classifier for the label. While training the classifier, we use the semi-supervised learning method with unlabeled images to build hierarchical classifiers. The unlabeled images can help clustering while we only have a few labeled training images. After training the classifier, we take the unlabeled image as the input of the classifiers to estimate the confidence values representing the possibility that the image is associated with the label. After using the approach with every label words, we can get the annotation from all of the label words. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T01:03:40Z (GMT). No. of bitstreams: 1 ntu-96-R94944019-1.pdf: 790397 bytes, checksum: e7904c6283fe04bb855e45a31e47d053 (MD5) Previous issue date: 2007 | en |
| dc.description.tableofcontents | 1.Introduction 1
1.1Motivation 3 1.2 Overview 5 2. Related Work 9 2.1 Related Work of Automatic Image Annotation 9 2.2 Related Work of Semi-Supervised Learning 11 3. Background 13 3.1 Watershed Segmentation 13 3.2 k-means Clustering 15 3.3 Visual-word-based Image Representation 16 3.3.1 Visual Words 17 3.3.2 The Generation of Image Feature 18 3.4 Sift Descriptor 20 3.5 pLSA 21 4. Classifier Training 23 4.1 Stopping Condition 27 4.2 Score Function 28 4.3 Splitting Method 30 5. Confidence value 31 6. Experiments 33 6.1 Datasets 33 6.2 Experimental Results 34 7. Conclusion and Future Work 39 8. Reference 40 | |
| dc.language.iso | en | |
| dc.subject | 影像檢索 | zh_TW |
| dc.subject | 影像標註 | zh_TW |
| dc.subject | 半指導式機器學習方法 | zh_TW |
| dc.subject | 階層式分類器 | zh_TW |
| dc.subject | 使用者反饋 | zh_TW |
| dc.subject | Image annotation | en |
| dc.subject | user feedback | en |
| dc.subject | hierarchical classifier | en |
| dc.subject | semi-supervised learning | en |
| dc.subject | Image retrieval | en |
| dc.title | 利用半指導式機器學習方法與階層式分類器的自動圖片標記法 | zh_TW |
| dc.title | Automatic Image Annotation Using a Semi-Supervised and Hierarchical Approach | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 95-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 洪一平 | |
| dc.contributor.oralexamcommittee | 唐政元,莊永裕,徐宏民 | |
| dc.subject.keyword | 影像標註,影像檢索,半指導式機器學習方法,階層式分類器,使用者反饋, | zh_TW |
| dc.subject.keyword | Image annotation,Image retrieval,semi-supervised learning,hierarchical classifier,user feedback, | en |
| dc.relation.page | 42 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2007-07-24 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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| ntu-96-1.pdf 未授權公開取用 | 771.87 kB | Adobe PDF |
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