Model-Driven Development of Content-Based Image Retrieval Systems

Beginning of article

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. Multiple client applications may be implemented to meet diverse user needs. Therefore, the aim of the current work is not to provide a particular graphical user interface for the system. Usually CBIR systems require the design of complex user interfaces to support user-friendly interaction with the CBIR core. Furthermore different technical environments, such as mobile devices, set special requirements on user-interfaces. Therefore, additional information apart from the basic functionality and data structure of the system has to be considered when modeling graphical user interfaces. For the model-driven design of advanced user interfaces a technique based on task models has been proposed in [21].

[FIGURE 1 OMITTED]

In the following sections the conceptual framework model for the different parts of the CBIR system and possibilities for its mapping onto an object-relational database management system (ORDBMS) are described.

2. Modeling of CBIR systems

For the design of almost each existing CBIR system a conceptual image data model has been used. These models have a lot in common, but very often they remain application specific, such as image models for the retrieval of medical or satellite image, images of human faces etc. Therefore, it has been an on-going aim for scientist to formalize a general image data model, which can be used for a broad range of application domains. Several domain-independent image data models have been developed in the early years of CBIR--AIR, VIMSYS, EMIR2. Brief overviews of these models are given in [4] and [20]. Another model is the object-oriented approach proposed in the DISIMA project from Oria and Ozsu [14], which represents mathematical formalizations for the different levels of abstraction and views of an image. Santini and Gupta [16] propose an extensible feature management engine for image retrieval with an own object-relational database model. Other modeling techniques for image databases are Summary Tree used in the PIQ [19] model and UCDL cited in [17]. One of the numerous existing multimedia conceptual data models (see [8] for an overview) is MPEG7 [1] and it has also been defined for representing image content. An evaluation of the quality of the models with respect to flexibility, completeness, validity, understandability and implementability showed that none of these models provides well defined extensibility and adaptability interfaces for deriving a domain-specific model of a CBIR system.

Therefore, a generic and adaptable conceptual model for image retrieval systems (GiACoMo-IRS), based on the UML modeling paradigm was defined to support the modeling of concrete CBIR systems. The model aims at providing a general base for representing image data structure and retrieval functionality, support the implementation on a large number of platforms, provide explicit mechanisms for extending and adapting the model for domain-specific applications and achieve good understandability through a modeling paradigm, supported by a wide range of modeling tools and a comprehensive tutorial for applying the model for deriving a domain-specific application model. The main reasons for choosing the Unified Modeling Language were: the extensibility of the model, integration possibilities with other models of system components, the support for modeling the system behavior, and last but not least its broad usage in modeling tools. Although the basic concepts of the UML model do not have an extensive support and notations for adaptability and extensibility interfaces, extensions of the UML model, which aim at providing adaptability and extensibility patterns for the design of frameworks are proposed in [3]. The UML-F profile from [3] is used for representing adaptability and extensibility interfaces in GiACoMo-IRS.

The term "framework model" is used here to represent a set of UML classes, relationships between them and operations which provide reusable software architecture and building blocks for deriving …