AN ANATOMY OF A LARGE-SCALE IMAGE SEARCH ENGINE
Wei-Cheng Lai, Edward Yi Chang, Kwang-Ting Tim Cheng · Series in machine perception and artificial intelligence · 2003
The content of the World-Wide-Web has moved rapidly from text-only to multimedia-rich. As information becomes available more and more as multimedia content, and requires more personalized access, we deem the existing infrastructures inadequate. To enable effective personalized search in Web-based or large-scale image libraries, this chapter proposes a perception-based search paradigm. We present the anatomy of a large-scale image search engine, which includes such a perception-based search component, a multi-resolution image-feature extractor, and a high-dimensional indexer, as well as traditional components such as a crawler and a keyword extractor. Through examples and empirical study, we show that our system is superior over traditional Content-Based Image Retrieval (CBIR) systems in three aspects: personalization, search accuracy and efficiency.