Course Catalog 2009-2010
Postgraduate

Basic Pori International Postgraduate Open University

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Course Catalog 2009-2010

SGN-5506 Multimedia Analysis and Retrieval, 5 cr

Person responsible

Serkan Kiranyaz, Moncef Gabbouj

Implementations

  Lecture times and places Target group recommended to
Implementation 1


Per 4 :
Thursday 12 - 14, TB110

 
Automaatiotekniikan koulutusohjelma
International Students
Jatko-opiskelijat
Signaalinkäsittelyn ja tietoliikennetekniikan koulutusohjelma
Sähkötekniikan koulutusohjelma
Tietojohtamisen koulutusohjelma
Tietotekniikan koulutusohjelma  


Requirements

Attendance and completion of 60% Exercises Final Exam

Principles and baselines related to teaching and learning

Regular lecture-based teaching methods with a personal assignment to apply the knowledge.

Learning outcomes

The main topic is the current approaches for searching, browsing, and mining various types of multimedia data such as images, audio and video (audiovisual data). The focus is on applying methods from machine learning and computer vision to these problems. In this course, a broad range of techniques will be studied including multimedia features, video analysis and management, retrieval techniques, spatial indexing methods, long-term learning and Relevance Feedback, audio analysis and retrieval, semantic-based retrieval techniques.

Content

Content Core content Complementary knowledge Specialist knowledge
1. With the growth of the Internet and developments in imaging technology, very large digital image and video archives have been created and used in numerous applications. Together with the increase in the number of pictorial archives, demands are also growing for methodologies and techniques to store and retrieve audio-visual data. Therefore, understanding and thereby analysis of multimedia content at the semantic level is the only way towards realizing the full potential of emerging digital media technologies aimed at the delivery of compelling multimedia solutions. The integration of knowledge, semantics and low-level multimedia processing for the purpose of automatic semantics extraction from multimedia content is still the subject of active research in academia and industry.  Data mining  Multimedia formats 


Evaluation criteria for the course

Assignments/Exercises : 30% Final Exam : 70% Project Work (optional) : 10%

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Study material

Type Name Author ISBN URL Edition, availability, ... Examination material Language
Book   Introduction to MPEG-7   B. S. Manjunath   0471 48678 7          English  
Book   Multimedia Information Retrieval and Management   D. Feng et al.            English  
Other literature           Relevant scientific papers and reference book material      English  


Prerequisites

Course Mandatory/Advisable Description
SGN-3010 Digitaalinen kuvankäsittely I Mandatory   Either one of the courses
SGN-3016 Digital Image Processing I Mandatory  
SGN-2500 Johdatus hahmontunnistukseen Mandatory   Either one of the courses
SGN-2506 Introduction to Pattern Recognition Mandatory  
SGN-3106 Digital Video Processing Mandatory    
SGN-3156 Video Compression Advisable    
SGN-4200 Digitaalinen audio Mandatory    
SGN-5016 Multimedia Signal Processing Advisable    
SGN-5306 Knowledge Mining Advisable    

Prerequisite relations (Requires logging in to POP)

Correspondence of content

Course Corresponds course  Description 
SGN-5506 Multimedia Analysis and Retrieval, 5 cr SGN-5507 Multimedia Analysis and Retrieval, 5-8 cr  

More precise information per implementation

  Description Methods of instruction Implementation
Implementation 1 Course Topic Current approaches for searching, browsing, and mining various types of multimedia data such as images, audio and video (audiovisual data). The focus is on applying methods from machine learning and computer vision on to these problems. In this course, a broad range of techniques will be studied including multimedia features, video analysis and management, retrieval techniques, spatial indexing methods, long-term learning and Relevance Feedback, audio analysis and retrieval, semantic-based retrieval techniques. Motivation and Scope With the growth of the Internet and developments in imaging technology, very large digital image and video archives have been created and used in numerous applications. Together with the increase in the number of pictorial archives, demands are also growing for methodologies and techniques to store and retrieve audio-visual data. Therefore, understanding and thereby analysis of multimedia content at the semantic level is the only way towards realizing the full potential of emerging digital media technologies aimed at the delivery of compelling multimedia solutions. The integration of knowledge, semantics and low-level multimedia processing for the purpose of automatic semantics extraction from multimedia content is still the subject of active research in academia and industry.