VISUAL INFORMATION SYSTEMS
CSE3VIS
2019
Credit points: 15
Subject outline
This subject covers an overview of visual information access, image representation, feature extraction, image recognition and understanding, and content-based image retrieval techniques. Design issues on facial image recognition and content-based image retrieval systems for image database management will be addressed, which contain eigenface technology, image feature extraction, indexing, similarity measure, lower-bounding lemma and performance evaluation. Practice on facial image recognition (FIR) will be offered in Labs.
School: School Engineering&Mathematical Sciences
Credit points: 15
Subject Co-ordinator: Lydia Cui
Available to Study Abroad Students: Yes
Subject year level: Year Level 3 - UG
Exchange Students: Yes
Subject particulars
Subject rules
Prerequisites: CSE2AIF or CSE2DBF
Co-requisites: N/A
Incompatible subjects: N/A
Equivalent subjects: N/A
Special conditions: N/A
Learning resources
Readings
| Resource Type | Title | Resource Requirement | Author and Year | Publisher |
|---|---|---|---|---|
| Readings | A First Course in Machine Learning | Recommended | Simon Rogers and Mark Girolami, 2016 | 2nd ED, Chapman and Hall/CRC |
| Readings | Pattern Recognition and Machine Learning | Recommended | Christopher M. Bishop, 2006 | Springer-Verlag New York |
Graduate capabilities & intended learning outcomes
01. Define the technologies used in visual information systems
- Activities:
- Students are required to complete the questions related to the specific information and knowledge in the exam papers
02. Learn the major issues in face recognition, content-based image database management, and describe how to effectively represent visual data for visual information processing
- Activities:
- Students are required to complete all questions related to the specific techniques for visual data modeling, face recognition and visual information retrieval in the exam papers
03. Demonstrate hands-on experience in developing a face recognition (FR) system based on eigenface technology
- Activities:
- one assignment on face recognition system design, and 8 laboratories exercises
04. Analyse the robustness of face recognition systems
- Activities:
- Students are required to understand the significance of robust face image recognition for real world applications
Melbourne, 2019, Semester 1, Day
Overview
Online enrolment: Yes
Maximum enrolment size: N/A
Enrolment information:
Subject Instance Co-ordinator: Lydia Cui
Class requirements
LectureWeek: 10 - 22
One 2.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
Laboratory ClassWeek: 11 - 22
One 2.0 hours laboratory class per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| one 3-hour examination | Hurdle requirement: To pass the subject, a pass in the examination is mandatory. | 70 | 01, 02 |
| One design report (750 words) | The assignment is about face recognition system design | 30 | 03, 04 |