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IMAGE RETRIEVAL

CSE5IR

2020

Credit points: 15

Subject outline

The explosive growth of digital media data has imposed unprecedented challenges for big multimedia data computing and management. In this subject, you will be introduced to a broad range concepts in image computing and retrieval for multimedia database management. You will learn and apply both the basics of digital image computing. Cutting-edge techniques for multimedia data understanding, content analysis, and image retrieval are practiced, applied, and discussed. With learnt knowledge and techniques, you have the skills to implement an image retrieval model.

School: Engineering and Mathematical Sciences (Pre 2022)

Credit points: 15

Subject Co-ordinator: Lydia Cui

Available to Study Abroad/Exchange Students: Yes

Subject year level: Year Level 5 - Masters

Available as Elective: No

Learning Activities: N/A

Capstone subject: No

Subject particulars

Subject rules

Prerequisites: CSE3CI OR CSE5ML OR CSE5CI

Co-requisites: N/A

Incompatible subjects: N/A

Equivalent subjects: N/A

Quota Management Strategy: N/A

Quota-conditions or rules: N/A

Special conditions: N/A

Minimum credit point requirement: N/A

Assumed knowledge: N/A

Learning resources

Digital Image Processing

Resource Type: Book

Resource Requirement: Recommended

Author: Rafael C. Gonzalez, Richard E. Woods

Year: 2017

Edition/Volume: 4th edition

Publisher: Pearson

ISBN: N/A

Chapter/article title: N/A

Chapter/issue: N/A

URL: N/A

Other description: N/A

Source location: N/A

Multimedia Information Retrieval and Management Technological Fundamentals and Applications

Resource Type: Book

Resource Requirement: Recommended

Author: D. Feng, W.C.Siu, and H.J.Zhang

Year: 2003

Edition/Volume: 2nd ED

Publisher: Chapman and Hall/ CRC

ISBN: N/A

Chapter/article title: N/A

Chapter/issue: N/A

URL: N/A

Other description: N/A

Source location: N/A

Career Ready

Career-focused: No

Work-based learning: No

Self sourced or Uni sourced: N/A

Entire subject or partial subject: N/A

Total hours/days required: N/A

Location of WBL activity (region): N/A

WBL addtional requirements: N/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

Intended Learning Outcomes

01. Apply image enhancement and representation techniques.
02. Implement critical components in content-based image retrieval systems and explain design issues by learnt image processing and analytics techniques for feature extraction, similarity measurement and indexing.
03. Design, implement and evaluate an image retrieval system with learned knowledge and techniques.
04. Research and explain the significance of robust image retrieval techniques for real-world applications.

Melbourne (Bundoora), 2020, Semester 1, Day

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Subject Instance Co-ordinator: Lydia Cui

Class requirements

Laboratory ClassWeek: 11 - 22
One 2.00 hours laboratory class per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.

LectureWeek: 10 - 22
One 2.00 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment elementCommentsCategoryContributionHurdle%ILO*

Assignment 1 - Design and implementation of an image retrieval model (1500 words equivalent)

N/AN/AN/ANo35SILO2, SILO3, SILO4

Assignment 2 - Presentation of designed image retrieval model (1000 words equivalent)

N/AN/AN/ANo15SILO4

One 2-hour exam (2000 words equivalent)

N/AN/AN/ANo50SILO1, SILO2