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PART II: DISTRIBUTED SIGNAL PROCESSING AND RECOGNITION

Cognition is a form of compression. In the past decades, the main thrust of communications has been evolving from one-to-many broadcasting to many-to-many peer-to-peer communications. We are seeing the trends that many-to-one communications is becoming an important driving force for the next generation of information technology industry. In the era of information overflow, technology users demand machines to help them interpret the signals they receive. While the fidelity of transmitted signals and network infrastructure are necessary, consumers are becoming keen to the content of communication data. A technology that delivers needed information to consumers can stand out as the winner. We consider machine cognition technology has to be integrated in the communication systems to help users to filter the data and, thus, significantly reduce the communication load.

  Large-Scale Video Semantic Routing and Sensor Network System

Since summer 2004, I joined the Distributed Computing department and have been working on a novel video semantic routing/filtering and sensor network system. We propose novel mechanism to reduce the amount of transmission loads based on the semantic user profiles. In other words, the system shall only transmit those video shots or stories that are of interest to the end users. Figure 1 shows an example of concept filtering and semantic routing for large-scale video streaming system. In the system, we deploy concept filters hierarchically based on the semantic trees. For instance, if an end user is interested at the basket clips, then the processing elements would first filter out all shots that are not sport-event, and then classify video packets to baseball, basketball, hockey, soccer, tennis, etc. Using this semantic routing structure, processing loads for each nodes can be reduced and thus make the overall system scale for large streaming environments. We also utilize this distributed video semanitc recognition framework to understand/manage visual information from visual sensor network. Our work is mainly focusing on the scalability and real time processing issues.

We propose to use the complexity-accuracy curves to optimally choose operating points in this semantic routing scenario. We also propose a set of novel video features, that result in better performance, in terms of both speed and accuracy, than our previous generic video concept classifiers. We have built one hundred concept classification filters. Experiments on 154 hours of video streams validated the effectiveness of the proposed system.

(Collaborators: Lisa Amini, Olivier Verschure, Anshul Sehgal)

Last Updated: 01/24/2006