KEYWORDS: Internet, Video, Transform theory, Data conversion, Computing systems, Data modeling, Control systems, Personal digital assistants, Prototyping, Explosives
The explosive growth of the Internet has come with increasing diversity and heterogeneity in terms of client device capability, network bandwidth, and user preferences. To date, most Web content has been designed with desktop computers in mind, and often contains rich media such as images, audio, and video. In many cases, this content is not suitable for devices like netTVs, handheld computers, personal digital assistants, and smart phones with relatively limited display capability, storage, processing power, and network access. Thus, Internet access is still constrained on these devices and there is a need to develop alternative approaches for information delivery. In this paper, we propose a framework for adaptive content delivery in heterogeneous environments. The goal is to improve content accessibility and perceived quality of service for information access under changing network and viewer conditions. The framework includes content adaptation algorithms, client capability and network bandwidth discovery methods, and a Decision Engine for determining when and how to adapt content. We describe this framework, initial system implementations based upon this framework, and the issues associated with the deployment of such systems based on different architectures.
An image retrieval system based on an information embedding scheme is proposed. Using relevance feedback, the system gradually embeds correlations between images from a high- level semantic perspective. The system starts with low-level image features and acquires knowledge from users to correlate different images in the database. Through the selection of positive and negative examples based on a given query, the semantic relationships between images are captured and embedded into the system by splitting/merging image clusters and updating the correlation matrix. Image retrieval is then based on the resulting image clusters and the correlation matrix obtained through relevance feedback.
Conference Committee Involvement (9)
Electronic Imaging and Multimedia Technology V
12 November 2007 | Beijing, China
Multimedia Content Analysis, Management, and Retrieval 2006
18 January 2006 | San Jose, California, United States
Multimedia Computing and Networking 2005
19 January 2005 | San Jose, California, United States
Storage and Retrieval Methods and Applications for Multimedia 2005
18 January 2005 | San Jose, California, United States
Electronic Imaging and Multimedia Technology IV
8 November 2004 | Beijing, China
Internet Multimedia Management Systems V
27 October 2004 | Philadelphia, Pennsylvania, United States
Storage and Retrieval Methods and Applications for Multimedia 2004
20 January 2004 | San Jose, California, United States
Internet Multimedia Management Systems IV
10 September 2003 | Orlando, Florida, United States
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