Multimedia Computing and Computer Vision Lab












Student Theses


Source Code / Datasets




Media Mining I

From Multimedia Computing Lab - University of Augsburg


Prof. Dr. Rainer Lienhart, Simon Hoffmann


The course addresses all aspects of computer algorithms that let a computer see, hear, learn, and understand audio-visual and multimedia data in the small and large scale. Small scale refers to individual media files or streams, while large scale refers to mining the web.

Mining media data is inherently a multidisciplinary field. Thus, the course will lay down the foundations of

  • Machine learning,
  • Image/video processing, and
  • Media content analysis.

The learned concepts will be illustrated by successful examples in practice. The accompanying exercises will contain some hands-on experiences. Towards the end of the course more advanced topics in object detection and object recognition will be addressed.

Outlook: The lectures Digital Image Processing and Bayesian Networks (former Media Mining II) can be used in SS 2008 to delve further into advanced topics in all three aspects of media mining.


  • Lecture: Tuesday, 8:15-9:45 and Thursday, 12:15-13:45, room 207 (Eichleitnerstr.); lecture starts on Oct., 16 2007 at 8:15am sharp.
  • Exercise: Wednesday, 12:15-13:45, room 202 (Eichleitnerstr.); first exercise will be on Oct, 31st 2007.
  • Additional lecture changes: No lecture on 7 Feb 2008; instead lecture is moved for Q&A (Questons & Answers) to 14 Feb 2008, 10am to 11:30am (closer to examine date). Also lecture on 5 Feb 2008 (“Faschingsdienstag”) is moved to 31. Jan 2008, 2:00 – 3:30pm, room 207 (Eichleitnerstr.) in order to give every student a change to attend the lecture.


Now open, please use LectureReg


Wednesday, 20 Feb 2008, 9:30am – 11:30am, Rooms: 207, 202 (Eichleitnerstr.)

In order to be admitted to the final exam, students are required:

  • to score at least 50% of the points archievable on the weekly assignments
  • to attend the weekly exercise session (Wednesday, 12:15-1:45pm). Students are allowed to miss the exercise session at most three times.

No exceptions!


6 SWS, Schein: yes, LP: 9

  1. Multimedia Teilbereiche:

Multimedia-Methoden, Multimedia-Anwendungen

Important Comments

  • All specified reading notes are relevant for the exams independent on how thoroughly they have been discussed during the lecture. Thus read them carefully.


  1. Mandatory reading: M. Mitchell. Machine Learning. McGraw-Hill Science/Engineering/Math; Chapters 1-8; (
  2. Mandatory reading: Jeff Hawkins, Sandra Blakeslee. On Intelligence. B&T; Auflage: Reprint (August 2005), ISBN-13: 978-0805078534
  3. Bernd Jähne. Digital Image Processing. Springer Verlag.
  4. David A. Forsyth and Jean Ponce. Computer Vision: A Modern Approach. Prentice Hall, Upper Saddle River, New Jersey 07458.( )
  5. Martin Schader and Stefan Kuhlins. Programmieren in C++. Springer-Verlag. ISBN 3540637761
    This is a perfect resource for all your questions relating C/C++; recommended if you are not skilled in C/C++

Online Material