Central Concerns and Questions

The objective of the research is to develop effective and efficient data analysis and retrieval techniques for various novel rich/multimedia application domains. The research challenges and questions include

• how to combine diverse knowledge to achieve effective information access
• how to improve efficiency and scalability of the related techniques
• economic and robust system performance evaluation process, and;
• deep understanding of business, cultural and social impacts of rich media

Emerging Ideas and Initiatives

The proliferation of various rich/multimedia information and associated applications over the past decade has led to an increasingly colorful lifestyle. Having been applied for wide range of purposes such as entertainment, education and psychology, unique characteristics of multimedia data pose huge challenge for informative retrieval, knowledge discovery and data management. Distinguished from standard alphanumeric data, rich/multimedia information has much more complex structure which might involve spatial and/or temporal dependency. Also, from representation point of view, rich/multimedia data could be huge and contain rich high level semantic meaning in general. Consequently, to efficiently manage and assess multimedia information under a real life application environment, there is an urgent need for technological advances in data structure for efficient organisation, intelligent content representation and system performance evaluation.

Selected Publications

[1] Jialie Shen. Stochastic Modeling Western Paintings for Effective Classification. To appear in Pattern Recognition.

[2] Jialie Shen, Dacheng Tao, Xuelong Li. Effective Video Event Detection via Subspace Projection. IEEE International Workshop on Multimedia Signal Processing (MMSP), October 2008 (invited paper).

[3] Bin Cui, Ling Liu, Calton Pu, Jialie Shen, Kian-Lee Tan. QueST: querying music databases by acoustic and textual features. ACM International Conference on Multimedia (ACM MM), October 2007, 1055-1064.

[4] Jialie Shen, Bin Cui, John Shepherd, Kian-Lee Tan. Towards Efficient Automated Singer Identification in Large Music Databases. ACM SIGIR Conference on Research & Development on Information Retrieval (ACM SIGIR), 2006, 59-66.

[5] Jialie Shen, John Shepherd. Efficient Benchmarking of Content-based Image Retrieval via Resampling. ACM International Conference on Multimedia (ACM MM), October 2006, 569-578.

Projects, Presentations and Posters

1. Jialie Shen, Indexing  Music  Database  via Multiple Acoustic Features (poster)
2. Jialie Shen, Efficient Performance Assessment of Content-based Image Retrieval: A   Resampling Based Approach (presentation)

Collaborations and Industry Linkages

1. Christos Faloutsos, Carnegie Mellon University
2. Ling Liu, Georgia Institute of Technology
3. Calton Pu, Georgia Institute of Technology
4. Yong Rui, Microsoft Research
5. Anindya Ghose, Leonard N. Stern School of Business, NYU