Video Search & Mining
(Data Mining and Information Retrieval)
  • 2012 Spring Semester Course
  • Instructor: Prof. Byoung-Tak Zhang
  • TAs: Jun Hee Yoo ( jhyoo@bi dot snu dot ac dot kr ), Ho-Sik Seok
  • Classroom: 302-107
  • Time: Tue & Thu, 3:30 pm - 4:45pm
  • Text:
    1. Video Search and Mining, D. Schonfeld, C. Shan, D. Tao, and L. Wang (Eds.), 2010.
    2. Video Mining (The International Series in Video Computing), Azriel Rosenfeld, David Doermann and Daniel DeMenthon (Eds.), 2003.
  • References:
    1. Video Content Analysis using Multimodal Information: For Movie Content Extraction, Indexing and Representation, Ying Li and C.C. Jay Kuo, 2010.
  • Evaluation:
    • Preliminary project poster and report (20%)
    • Final project poster and report (20%)
    • Paper presentations (30%)
    • 1 open-book exam (20%)
    • Attendance and discussion (10%)
  • Announcement
    • (4/17): Prof. Zhang will present Machine Learning instead of paper presentation.
    • (4/24): T.A. will present Ch.8 instead of Hyun-Woo Song.
    • (5/3): Byoung-Hee Kim will present "Deep Learning Model" instead of Image Search Practice 4.
    • (5/9): Upload templates for final reports and final poster. -here-
    • (5/29): Exam date is changed to (5/31)
                 Link fixed (Video Summarization PPT files)
    • (6/3): Schedule is changed. (6/5, 6/7, 6/12, 6/14) no class. Do your project, poster2, report2.
    • (6/28) : Scoring is finished. please check here.
  • Objectives
    • The amount of multimedia data containing images, sounds, audio, speech, and video is rapidly increasing due to the widespread use of smart phones and digital cameras combined with mobile webs and social networks. Making sense of these multimedia data is fundamentally important not just for applications in education, arts, entertainment, and web services, but also for basic research in cognitive science, robotics, human-computer interaction, and artificial intelligence. This course gives an introduction to data mining and information retrieval with an emphasis on video search and mining. Course attendants will study a variety of video mining systems to learn the basic algorithms and machine learning techniques to analyze video, image, and audio data with hands-on experience with software systems.

  • Lecture Schedule
  • Week Topics Presenter Slides
    Week 1 3/6
    • Course Outline
    • Video Search and Mining: Overview
    • Assignment of Paper Reading
       
    3/8
    • MATLAB Practice 1
      PPT file
    Week 2 3/13
    • Data Mining & Information Retrieval
      PPT file1
    PPT file2
    3/15
    • Machine Learning for Video Mining and Retrieval
      PPT file1
    PPT file2 
    Week 3 3/20
    • Efficient Video Browsing (Ch. 1)
    구해모 PPT file
    3/22 MATLAB Practice 2   PPT file
    Week 4 3/27
    • Temporal Video Boundaries (Ch. 3)
    이상선 PPT file
    3/29 Image Search Practice 1 PPT file
    Week 5 4/3
    • Video Summarization (Ch. 4)
    정승근 PPT file
    4/5
    • Mining TV Broadcasts 24/7 for Recurring Video Sequences
    Lars Holdaas PPT file
    Week 6 4/10
    • Movie Content Analysis, Indexing and Skimming (Ch. 5)
    • Deadline for Project Report 0 (4/10)
    Sabina Andersson PPT file
    4/12
    • Face Recognition and Retrieval in Video
    권민혁 PPT file
    Week 7 4/17
    • Video Categorization using Semantics (Ch. 7)
    • Machine Learning Tutorial
    김덕주
    장병탁
    PPT file
    4/19 Image Search Practice 2 ZIP file
    Week 8 4/24
    • Understanding the Semantics of Media (Ch. 8)
    송현우
    유준희
    PPT file
    4/26 Image Search Practice 3 PPT file
    Week 9 5/1
    • Project Poster Presentation 1
       
    5/3 Image Search Practice 4
    Deep Learning Models
    김병희 PDF file
    Week 10 5/8
    • Statistical Techniques for Video Analysis and Searching (Ch. 9)
    이대근 PDF file
    5/10
    • Project Practice 1
    • Deadline for Project Report 1 (5/10)
    Week 11 5/15
    • Unsupervised Mining of Statistical Temporal Structures in Video (Ch. 10)
    Zeyuan Liu PPT file
    5/17 Project Practice 2
    Week 12 5/22
    • Pseudo-Relevance Feedback for Multimedia Retrieval (Ch. 11)
    Mwangi Simon Kariuki PPT file
    5/24
    • No Class
    Week 13 5/29
    • Visual Concept Learning from Weakly Labeled Web Videos
    Tae Inky PDF file
    5/31
    • Exam
    Week 14 6/5
    • No Class
       
    6/7
    • No Class
    Week 15 6/12
    • No Class
       
    6/14
    • No Class
    Week 16 6/19
    • Poster Presentation 2
    • Deadline for Project Report 2 (6/19)
This page is maintained by Jun Hee Yoo
Last update: 2012.6.3.