Increasing application demands are pushing databases toward providing effective and efficient support for content-based retrieval over multimedia objects. In addition to adequate retrieval techniques, it is also important to enable some form of adaptation to users' specific needs. This paper introduces a new refinement method for retrieval based on the learning of the users' specific preferences. The proposed system indexes objects based on shape and groups them into a set of clusters, with each cluster represented by a prototype. Clustering constructs a taxonomy of objects by forming groups of closely-related objects. The proposed approach to learn the users' preferences is to refine corresponding clusters from objects provided by the users in the foreground, and to simultaneously adapt the database index in the background. Queries can be performed based solely on shape, or on a combination of shape with other features such as color. Our experimental results show that the system successfully adapts queries into databases with only a small amount of feedback from the users. The quality of the returned results is superior to that of a color-based query, and continues to improve with further use.

This paper presents a unified image analysis approach for automated detection, segmentation, and classification of breast cancer nuclei using a neural network, which learns to cluster shapes and to classify nuclei. The proposed neural network is incrementally grown by creating a new cluster whenever a previously unseen shape is presented. Each hidden node represents a cluster used as a template to provide faster and more accurate nuclei detection and segmentation. Online learning gives the system improved performance with continued use. The effectiveness of the resulting system is demonstrated on a task of cytological image analysis, with classification of individual nuclei used to diagnose the sample. This demonstrates the potential effectiveness of such a system on diagnostic tasks that require the classification of individual cells.

This paper introduces a personalization method for image retrieval based on the learning of personal preferences. The proposed system indexes objects based on shape and groups them into a set of clusters, or prototypes. Our personalization method refines corresponding prototypes from objects provided by the user in the foreground, and simultaneously adapts the database index in the background.

The detection, precise segmentation and classification of specific objects is an important task in many computer vision and image analysis problems, particularly in medical domains. Existing methods such as template matching typically require excessive computation and user interaction, particularly if the desired objects have a variety of different shapes. This paper presents a new approach that uses unsupervised learning to find a set of templates specific to the objects being outlined by the user. The templates are formed by averaging the shapes that belong to a particular cluster, and are used to guide an intelligent search through the space of possible objects. This results in decreased time and increased accuracy for repetitive segmentation problems, as system performance improves with continued use. Further, the information gained through clustering and user feedback is used to classify the objects for problems in which shape is relevant to the classification. The effectiveness of the resulting system is demonstrated on two applications: a medical diagnosis task using cytological images and a vehicle recognition task.