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研究生： 廖雅蓮
Ya-Lien Liao
論文名稱： A Meta-Feature Representation Approach toImage Annotation
指導教授： 蔡志豐
Chih-Fong Tsai
口試委員:
學位類別： 碩士
Master
系所名稱： 管理學院 - 資訊管理學系
Department of Information Management
畢業學年度： 98
語文別： 英文
論文頁數： 86
中文關鍵詞： 圖片特徵表示法 、影像檢索 、圖片命名 、圖片分類
外文關鍵詞： feature representation, image retrieval, image annotation, image classification
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    • 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。

    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.

    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

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