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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 44236 (19)
- University: Sharif University of Technology
- Department: Computer Engineering
- Advisor(s): Jamzad, Mansour
- Abstract:
- Scene classification is one of the most controversial fields in computer vision. It has many applications such as robot navigation and control, content-based image retrieval (CBIR), semantic organization of image databases, depth estimation and multimedia services. In fact the outcome of any classification system depends on the ability of the feature vector defined for the problem, by means of its distinguishing strength. In this research we focus on efficient feature extraction methods. In recent years, methods based on bags of features and special pyramid approach, have shown good performance in scene classification comparison to the others. So we based our proposed method on these ideas. There are three new suggestions in this thesis.
Multi-block local binary patterns and multi-block local derivative patterns are two proposed methods for scene texture classification. In addition we implemented the color property of images in bags of color features. Experimental results showed that combination of these three methods based on the idea of bags of feature had a good performance. Using dense feature extraction and One-class SVM classifier with nonlinear kernel is another distinguishing issue in our works. The results of the proposed methods have been compared with the state of the art methods in this field and have shown improvements in performance - Keywords:
- Scene Classification ; Multiblock Local Binary Pattern ; Multiblock Local Derivative Pattern ; Feature Color Bag ; Spatial Pyramid
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