For complex data types such as multimedia, traditional data management methods are not suitable. Instead of attribute matching approaches, access methods based on object similarity are becoming popular. Recently, this resulted in an intensive research of indexing and searching methods for the similarity-based retrieval. Nowadays, many efficient methods are already available, but using them to build an actual search system still requires specialists that tune the methods and build the system manually. Several attempts have already been made to provide a more convenient high-level interface in a form of query languages for such systems, but these are limited to support only basic similarity queries. In this paper, we propose a new language that allows to formulate content-based queries in a flexible way, taking into account the functionality offered by a particular search engine in use. To ensure this, the language is based on a general data model with an abstract set of operations. Consequently, the language supports various advanced query operations such as similarity joins, reverse nearest neighbor queries, or distinct kNN queries, as well as multi-object and multi-modal queries. The language is primarily designed to be used with the MESSIF framework for content-based searching but can be employed by other retrieval systems as well.
One of the most important bits of every search engine is the query interface. Complex interfaces may cause users to struggle in learning the handling. An example is the query language SQL. It is really powerful, but usually remains hidden to the common user. On the other hand the usage of current languages for Internet search engines is very simple and straightforward. Even beginners are able to find relevant documents. This paper presents a hybrid query language suitable for both image and text retrieval. It is very similar to those of a full text search engine but also includes some extensions required for content based image retrieval. The language is extensible to cover arbitrary feature vectors and handle fuzzy queries. The visual database query language QBE (query by example) is a classical, declarative query language based on the relational domain calculus. However, due to insufficient support of vagueness QBE is not an appropriate query language for formulating similarity queries required in the context of multimedia databases. In this work we propose the query language WS-QBE which combines a schema to weight query terms as well as concepts from fuzzy logic and QBE into one language. WS-QBE enables a visual, declarative formulation of complex similarity queries. The semantics of WS-QBE is defined by a mapping of WS-QBE queries onto the similarity domain calculus SDC which is proposed here, too. The paper discusses the design and implementation of the oquel query language for content based image retrieval. The retrieval process takes place entirely within the ontological domain defined by the syntax and semantics of the user query. Since the system does not rely on the pre-annotation of images with sentences in the language, the format of text queries is highly flexible. The language is also extensible to allow for the definition of higher level terms such as "cars", "people", "buildings", etc. on the basis of existing language constructs. Images are retrieved by deriving an abstract syntax tree form of a textual user query and probabilistically evaluating it by analysing the composition and perceptual properties of salient image regions in light of the query. The matching process utilises automatically extracted image segmentation and classification information and can incorporate any other feature extraction mechanisms or contextual knowledge available at processing time.