Multimedia Retrieval

Semester:

Winter Term 2017/2018

Lecturer:

Place/Time:

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	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.

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