Thesis Topic Details

Topic ID:
810
Title:
Semantic Video Understanding Based on HMM framework
Supervisor:
Jian Zhang
Research Area:
Artificial Intelligence
Associated Staff
Assessor:
Ying Zhang
Topic Details
Status:
Active
Type:
R & D
Programs:
CS CE BIOM BINF SE
Group Suitable:
No
Industrial:
No
Pre-requisites:
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Description:
Content based video analysis has attracted my research groups to explore the semantic representation for multimedia content. There are several key low feature descriptions defined in MPEG-7 standard with its descriptors. One challenge to multimedia research is how to use these low level features to achieve some semantic understanding of the video sequence. This will involve the low-level feature extraction, Gaussian model expression, and HMM model training and testing, which will support to generate the semantic understanding of a video sequence automatically. The knowledge you learned from the courses of computer vision, pattern recognition and multimedia technology such as image/video processing can all find their utilization in this project. Your programming skills will also be developed as you will develop a demo in software.

Comments:
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