Abstract Content‐based image retrieval (CBIR) is a process of retrieving images from an image database by exploiting the content of the images (typically the querying of an image). CBIR avoids many problems associated with traditional ways of retrieving images by keywords. Thus, a growing interest in the area of CBIR has been established in recent years. In this paper, a novel object‐oriented framework (CBIRFrame) is built for CBIR applications development. We discuss the motivations for CBIRFrame before discussing its design in detail. Two applications of CBIRFrame are also briefly discussed to show the effectiveness of applying CBIRFrame to real applications. Finally, we outline the possible uses of the design of CBIRFrame for other types of domains, such as content‐based retrieval of video clips. Copyright © 2003 John Wiley & Sons, Ltd.

The I-Browse project aimed to develop a prototype system to provide facilities for supporting intelligent retrieval of medical images through a combination of iconic and semantic content. The resulting prototype system, I-Browse, is able to extract and represent relevant iconic and semantic information from input images and to automatically generate textual annotations for images. Techniques were also developed for retrieving relevant images from the database given either an example image query or a textual query. The facilities provided by I-Browse were evaluated by medical colleagues and judged to have the potential of alleviating some of the time-consuming tasks that doctors now have to perform daily and providing a means of identifying previously unknown relationships between visual appearance and histological events.

We present a semantic content representation scheme and the associated techniques for supporting (a) query by image examples or by natural language in a histological image database and (b) automatic annotation generation for images through image semantic analysis. In this research, various types of query are analysed by either a semantic analyser or a natural language analyser to extract high level concepts and histological information, which are subsequently converted into an internal semantic-content representation structure code-named "Papillon". Papillon serves not only as an intermediate representation scheme but also stores the semantic content of the image that will be used to match against the semantic index structure within the image database during query processing. During the image database population phase, all images that are going to be put into the database will go through the same processing so that every image would have its semantic content represented by a Papillon structure. Since the Papillon structure for an image contains high level semantic information of the image, it forms the basis of the technique that automatically generates textual annotation for the input images. Papillon bridges the gap between different media in the database, allows complicated intelligent browsing to be carried out efficiently and also provides a well-defined semantic content representation scheme for different content processing engines developed for content-based retrieval.

Medical images are usually composed of different kinds of texture components which are always so much varied that a conventional single window approach cannot capture enough salient information for comparison. This paper applies the widely used multi-channel Gabor filters to demonstrate how a multi-window approach can improve the classification accuracy rate of histological labels. In addition, a most confident window method is proposed to further increase the accuracy rate of the multi-window approach.

A gastro-intestinal (GI) tract histological image is usually composed of texture components with different dimensions and properties. To analyze a histological image, we divide it into an array of sub-images. A feature vector comprising a set of Gabor filters and the intensity statistics is computed in order to classify each sub-image to one of 63 histological labels. To retrieve an image from the database, we compare three similarity measures, shape, neighbour and sub-image frequency distribution. It is found that both neighbour and sub-image frequency distribution similarity measures perform similarly well but the shape similarity measure yields the worst result when retrieving images of different GI tract organs. In general, the sub-image frequency distribution measure is the best choice because it requires less time to compute than the neighbour measure.

Reports a generic object-oriented framework for content-based image retrieval (CBIR) systems. It is designed so that the basic data structures and functionality of a typical CBIR system are provided without sacrificing speed and flexibility. The framework is based on a five-tier architecture that allows modules in different tiers to be developed independently, and thus flexibility is ensured. We show that our framework is able to adapt to a wide range of CBIR applications by applying the framework to the development of two on-going projects: a trademark image retrieval system and a medical (histological) image retrieval system. These applications are briefly discussed.

This paper presents a unified method for detecting both reflection-symmetry and rotation-symmetry of 2D images based on an identical set of features, i.e., the first three nonzero generalized complex (GC) moments. This method is theoretically guaranteed to detect all the axes of symmetries of every 2D image, if more nonzero GC moments are used in the feature set. Furthermore, we establish the relationship between reflectional symmetry and rotational symmetry in an image, which can be used to check the correctness of symmetry detection. This method has been demonstrated experimentally using more than 200 images.

We present a technique of content-based image retrieval based on the symmetric property of an image and its generalized complex moments. We propose a novel symmetry detection algorithm that is both fast and general compared with existing symmetry detection algorithms. Given the sensitivity of human perception to symmetry, the use of symmetry class helps to ensure that the retrieved images are visually similar. Experimental results show that these features are effective in retrieving binary images and our similarity function is able to rank them first place for nearly all the sample queries used in our experiment.

We present an efficient algorithm for detecting all the symmetry axes of a 2D planar shape. We proved theorems relating to certain distinguishing properties of the generalised complex moments computed for symmetrical objects. Based on these properties, the detection of symmetry axes can be carried out efficiently and accurately. The algorithm can also be applied to detecting partially (perceptually) symmetrical objects.