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During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.
Unit of study_
COMP5425: Multimedia Retrieval
The explosive growth of multimedia data, including text, audio, images and video has imposed unprecedented challenges for search engines to meet various information needs of users. This unit provides students with the necessary and updated knowledge of this field in the context of big data, from the information retrieval basics of a search engine, to many advanced techniques towards next generation search engines, such as content based image and video retrieval, large scale visual information retrieval, and social media.
| Code | COMP5425 |
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| Academic unit | Computer Science |
| Credit points | 6 |
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Prerequisites:
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None |
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Corequisites:
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None |
| Prohibitions:
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None |
| Assumed knowledge:
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Experience with programming skills, as learned in COMP9103 OR COMP2123 OR COMP2823 OR INFO1105 OR INFO1905 (or equivalent UoS from different institutions). |
At the completion of this unit, you should be able to:
- LO1. perform functional analysis for specific application domain and specific users
- LO2. conduct literature review in the field related to a given task
- LO3. perform function design of a retrieval system
- LO4. design technical solutions to solve a media retrieval problem with learned knowledge and techniques
- LO5. explain the framework and key components of a general retrieval systems
- LO6. reflect on the state of the art in multimedia retrieval
- LO7. evaluate the advantages and shortcomings of a specific retrieval technique and solution
- LO8. practice popular algorithms related to retrieval techniques, such as feature extraction and similarity measurement.
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