In this work, we are interested in technologies that will allow users to actively browse and navigate large image databases and to retrieve images through interactive fast browsing and navigation. The development of a browsing/navigation-based image retrieval system has at least two challenges. The first is that the system's graphical user interface (GUI) should intuitively reflect the distribution of the images in the database in order to provide the users with a mental picture of the database content and a sense of orientation during the course of browsing/navigation. The second is that it has to be fast and responsive, and be able to respond to users actions at an interactive speed in order to engage the users. We have developed a method that attempts to address these challenges of a browsing/navigation based image retrieval systems. The unique feature of the method is that we take an integrated approach to the design of the browsing/navigation GUI and the indexing and organization of the images in the database. The GUI is tightly coupled with the algorithms that run in the background. The visual cues of the GUI are logically linked with various parts of the repository (image clusters of various particular visual themes) thus providing intuitive correspondences between the GUI and the database contents. In the backend, the images are organized into a binary tree data structure using a sequential maximal information coding algorithm and each image is indexed by an n-bit binary index thus making response to users' action very fast. We present experimental results to demonstrate the usefulness of our method both as a pre-filtering tool and for developing browsing/navigation systems for fast image retrieval from large image databases.

Functional neuroimaging is a powerful biological tool to investigate the regions of the brain responsible for performing diflerent mental functions. The traditional anterpretation method is statistical analysis in the spatial domain, which is computationally expensive. In this paper we present a hybrid wavelet/neural network scheme to analyze functional brain images. Features are extracted in the Wavelet domain and then fed t o a neural network for detection. The proposed method is examined by exploring the diflerences between Positron Emission Tomography (PET) images acquired under different ezperimental conditions during a tone recognition task. The performance shows its potential in the fast developing functional neuroimaging area.

-We present a storage-efficient and computationally fast method for rapid navigation/browsing through large image repositories and for content-based image retrieval. In the developed system, multiple resolution and orientation achromatic and opponent chromatic channels are sequentially encoded by a maximal information sensory encoding model, which conveniently and effectively indexes the images into a binary tree data structure. Content-based image retrieval, database navigation and image browsing are done very efficiently and rapidly by manipulating the n-bit binary keys in the binary tree data structure. We present experimental results to demonstrate the effectiveness of our method.