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Multimedia Retrieval

Winter Term 2017/2018
Lecture: H-F 001, Exercise: H-F 107/108
Recommended for: 
Bachelor Students of Computer Science

Lecture Materials
Lecture Materials

Practice Manager: 
Exercise operational: 

Exercise Materials

Topic Date Slides Etc.
Introduction and OpenCV Installation 19.10    
Basic Knowledge about Images in OpenCV 26.10    
Different Image Processing Functions 02.11    
Query by Example: Color Histogram Extraction 09.11    
Query by Example: Similarity Computation 16.11    
Continued (Implementing the retrieval system) 23.11   If you have already implemented the retrieval system, please try to extract additional edge features (HOG: Histogram of Oriented Gradients) by referring to [1] and [2]
Query by Example: Evaluating Retrieval Results 30.11    
ImageClassification: Support Vector Machine 06.12    
Image Classification: Implementing SVM-based Classification 21.12    
Continued (Implementing SVM-based Classification) 11.01    
Local Features (SIFT: Scale-Invariant Feature Transform) 18.01   Compared to the original slides I used at the lecture, these uploaded slides miss many images that may be related to copyright issues. If you want to have the original slides, please e-mail me. 
Local Features: SIFT Feature Extraction by OpenCV 25.01    
Local Features: Feature Matching 01.02    

Additional Information:

  • Please take your own laptops to the exercise.
  • Programming skill for C is necessary for implementing the codes (C++ is desirable, but is not necessary).
  • If you want to use other programming languages like java and python, I will support you as much as possible.
  • In the oral examination, you will be asked several questions about the exercise course.