Model-Driven Development of Content-Based Image Retrieval Systems

Article excerpt

ABSTRACT: Generic systems for content-based image retrieval (CBIR), such as QBIC [7] cannot be used to solve domain-specific image retrieval problems, as for example, the identification of manuscript writers based on the visual characteristics of their handwriting. Domain-specific CBIR systems currently have to be implemented bottom up, i.e. almost from scratch, each time a new domain-specific solution is sought. Inspired by the recognition, that CBIR systems, although developed for different domain-problems, comprise similar building blocks and architecture, the idea of adopting model-driven development techniques for generating CBIR systems was elaborated. To support the design of domain-specific CBIR-Systems on a conceptual level by reusing data structure and functional interfaces a framework model is developed, which can be used to derive concrete domain-specific CBIR models. A transformation approach for the generation of a platform-specific implementation on top of an object-relational database from the concrete conceptual model is proposed. Finally, how these techniques can be applied for the design of a CBIR system for the identification of music manuscript writers based on the visual characteristics of their handwriting is demonstrated.

Classification and subject descriptors

I 4 [Image Processing and Computer Vision]; H 3.1 [Content Analysis and Indexing] I 4.10 [Image representation]

General Terms

Image processing, Content development, Image retrieval systems

Keywords: model-driven development, content-based image retrieval

1. Introduction

During the research project eNoteHistory [2], in which a specialized CBIR system for the identification of writers of historical music manuscript was designed and implemented, existing CBIR systems were studied and classified according to their purpose into the following categories. Generic CBIR Systems (e.g., QBIC [7], imgSeek [10], IMatch [15]) make use of generic low-level features such as color, texture and shape and are not suited for carrying out a specialized image retrieval task. Specialized CBIR Systems, such as a system for recognizing similar images in a set of 2D-Electrophoresis Gel Images are implemented only for a special domain and are normally highly effective for this domain, but cannot be applied effectively in any other applications. CBIR frameworks (e.g., GIFT [13], PicSOM [11], VizIR [6]) offer extensible software architectures for developing domain-specific CBIR application. However, these frameworks are implemented for special platforms and do not offer flexible data storage possibilities.

The result of adapting these frameworks for a specific domain application is not a compact, specialized application, but rather an extended version of the large framework application.

To facilitate the development of tailor-made domain-specific CBIR systems the idea of incorporating model-driven development techniques for modeling and generating CBIR systems for various implementation platforms was investigated. This approach could be very useful for building scientific image retrieval applications, where images originate from various specialized domains. In Figure 1 an overview of the model-driven development architecture for CBIR systems is shown. Two main groups of techniques, which have to be provided, can be distinguished.

The first group comprises components for creating a platform independent model of the CBIR system. These components make use of the framework model proposed in this paper. The framework model provides a starting point for the conceptual modeling of the complex data structures, storage and retrieval operations of CBIR systems. The second group of techniques comprises components for transforming the concrete CBIR system conceptual model into a specific implementation. The generated core data structure and functionality of the CBIR system can be used by different client applications. …