MALCBR: Content-Based Retrieval of image databases at multiple abstraction levels

Abstract

Content-based search of large image database has received significant attention recently. In this paper, we proposed a new framework, Multiple Abstraction Level Content Based Retrieval (MALCBR), for specifying and process contentbased retrieval queries on databases of images, time series, or video data. This framework allows search targets to be expressed in a object-based fashion, that allows the extensible specification of arbitrarily complex queries. In our approach, the search targets are either simple objects, specified at multiple levels of abstraction (pixel, feature and semantic levels), or composite objects, defined as collections of relation on the elements of a set of simple objects. During the search, simple objects at the semantic level are retrieved from database tables, feature level objects are computed using pre-extracted features, appropriately indexed, and pixel level objects are extracted from the raw data. Composite objects are computed at query execution time. This framework provides a powerful mechanism for specifying complicated search target and enable efficient processing of filtering of the search results.