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Image Classification using SVM Classifier Learned by AdaBoost Method : AdaBoost 방법을 통해 학습된 SVM 분류기를 이용한 영상 분류
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 유석인 | - |
dc.contributor.author | 이해나 | - |
dc.date.accessioned | 2017-07-14T03:00:15Z | - |
dc.date.available | 2017-07-14T03:00:15Z | - |
dc.date.issued | 2015-02 | - |
dc.identifier.other | 000000026006 | - |
dc.identifier.uri | https://hdl.handle.net/10371/123156 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 유석인. | - |
dc.description.abstract | This thesis presents the algorithm that categorizes images by objects contained in the images. The images are encoded with bag-of-features (BoF) model which represents an image as a collection of unordered features extracted from the local patches. To deal with the classification of multiple object categories, the one-versus-all method is applied for the implementation of multi-class classifier. The object classifiers are built as the number of object categories, and each classifier decides whether an image is included in the object category or not. The object classifier has been developed on the AdaBoost method. The object classifier is given by the weighted sum of 200 support vector machine (SVM) component classifiers. Among multiple object classifiers, the classifier with the highest output function value finally determines the category of the object image. The classification efficiency of the presented algorithm has been illustrated on the images from Caltech-101 dataset. | - |
dc.description.tableofcontents | Abstract i
Contents iii List of Figures v List of Tables vi Chapter 1 Introduction 1 Chapter 2 Related Work 3 2.1 Image classification approaches . . . . . . . . . . . 3 2.2 Boosting methods . . . . . . . . . . . . . . . 6 2.3 Background . . . . . . . . . . . . . . . . . 9 2.3.1 Support vector machine . . . . . . . . . . . . . 9 Chapter 3 Proposed Algorithm 12 3.1 SIFT feature extraction . . . . . . . . . . . . . 13 3.2 Codebook construction . . . . . . . . . . . . . 15 3.3 Bag-of-features representation . . . . . . . . . . . 16 3.4 Classifier design . . . . . . . . . . . . . . . 16 Chapter 4 Experiments 20 4.1 Dataset . . . . . . . . . . . . . . . . . . 20 4.2 Bag-of-features representation . . . . . . . . . . . 22 4.3 Classifiers . . . . . . . . . . . . . . . . . 24 4.4 Classification results . . . . . . . . . . . . . . 25 Chapter 5 Conclusion 29 Bibliography 30 Abstract in Korean 34 | - |
dc.format | application/pdf | - |
dc.format.extent | 1674621 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | Image classification | - |
dc.subject | Object category recognition | - |
dc.subject | Bag-of-features (BoF) model | - |
dc.subject | AdaBoost | - |
dc.subject | Support vector machine (SVM) | - |
dc.subject.ddc | 621 | - |
dc.title | Image Classification using SVM Classifier Learned by AdaBoost Method | - |
dc.title.alternative | AdaBoost 방법을 통해 학습된 SVM 분류기를 이용한 영상 분류 | - |
dc.type | Thesis | - |
dc.contributor.AlternativeAuthor | Hae-na Lee | - |
dc.description.degree | Master | - |
dc.citation.pages | vi, 34 | - |
dc.contributor.affiliation | 공과대학 전기·컴퓨터공학부 | - |
dc.date.awarded | 2015-02 | - |
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