Author Description

Another aspect of our research considers using similarity search to perform classification under limited computation time and variable response rates. In such contexts, anytime algorithms, amenable to variable response times by exchanging quality of response as a function of time, have been found to be especially useful. We present a generalized framework which utilizes a scoring function that estimates the intermediate result quality of an object being classified. Our contribution extends existing anytime algorithms to concurrent queries by dynamically scheduling computational resources for each object. We show that the lack of such inter-object consideration would otherwise result in poor allocation of computation time and lead to reduced performance.