An Anatomy of a Large-Scale Image Search Engine

As the World-Wide Web moves rapidly from text-based towards multimedia content, and requires more personalized access, we deem existing infrastructures inadequate. In this paper, we present critical components for enabling effective searches in Web-based or large-scale image libraries. In particular, we propose a perception-based search component, which can learn users’ subjective query concepts quickly through an intelligent sampling process. Through an example, we demonstrate how, and explain why our perception-based search paradigm can effectively personalize a query and achieve high recall.

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