Analysis and Evaluation of Search Efficiency for Image Databases

The problem of image identification by content from an image database is viewed as a search space reduction problem where different paradigms and approaches are employed to progressively reduce the search space. Unlike information recovery, such reduction cannot in general be used to pinpoint exactly all and only those images that are needed by a query. In this paper, three main search space reduction strategies are evaluated: high-level descriptive approach, low-level signatures comparison, and user-level picture keys. The effectiveness of these approaches are studied and quantified. Based on these strategies, it is possible that highly effective query models may be built. In combination, they offer the potential to drastically reduce the search space for image content identification through the use of ranking and relevance feedback techniques.