1 Visible Image Retrieval

The emergence of multimedia, the availability of large digital archives, as well as the rapid growth of the World-Wide Web, have recently attracted research efforts in providing tools for effective retrieval of image data based on their content (Content-Based Image Retrieval, CBIR). The relevance of CBIR for many applications, ranging from art galleries and museum archives, to picture/photograph, medical and geographic databases, criminal investigation, intellectual property and trademarks, fashion and interior design, makes this research field one of the fastest growing in information technology. Yet, after a decade of intensive research, CBIR technologies – save perhaps for very specialized areas such as crime prevention, medical diagnosis or fashion design – have had a limited impact on real-world applications. For instance, recent attempts to enhance text-based search engines on the WWW with CBIR options highlight both an increasing interest in the use of digital imagery and the current limitations of general-purpose image search facilities. This chapter reviews applications and research themes in Visible Image Retrieval, namely, retrieval by content of heterogeneous collections of single images generated with visible spectrum technologies. It is generally agreed that a key design challenge in the field is how to reduce the semantic gap between user expectation and system support, especially in non-professional applications. Recently, the interest in sophisticated image analysis and recognition techniques as a way to enhance the built-in intelligence of systems has been greatly reduced in favour of new models of human perception, and advanced human-computer interaction tools aimed at exploiting the user’s intelligence and understanding of the retrieval task at hand. A careful image domain and retrieval task analysis is also of great importance to ensure that queries are formulated at a semantic level appropriate for a specific application. A number of examples encompassing different semantic levels and application