FDive: Learning Relevance Models Using Patternbased Similarity Measures The detection of interesting patterns in large highdimensional datasets is difficult because of their dimensionality and pattern complexity. Therefore, analysts require automated support for the extraction of relevant patterns. In this paper, we present FDive, a visual active learning system that helps to create visually explorable relevance models, assisted by learning a patternbased similarity. We use a small set of userprovided labels to rank similarity measures, consisting of feature descriptor and distance function combinations, by their ability to distinguish relevant from irrelevant data. Based on the bestranked similarity measure, the system calculates an interactive SelfOrganizing Mapbased relevance model, which classifies data according to the cluster affiliation. It also automatically prompts further relevance feedback to improve its accuracy. Uncertain areas, especially near the decision boundaries, are highlighted and can be refined by the user. We evaluate our approach by comparison to stateoftheart feature selection techniques and demonstrate the usefulness of our approach by a case study classifying electron microscopy images of brain cells. The results show that FDive enhances both the quality and understanding of relevance models and can thus lead to new insights for brain research.

Multiple Query ContentBased Image Retrieval Using Relevance Feature Weight Learning We propose a novel multiple query retrieval approach, named weightlearner, which relies on visual feature discrimination to estimate the distances between the query images and images in the database. For each query image, this discrimination consists of learning, in an unsupervised manner, the optimal relevance weight for each visual featuredescriptor. These feature relevance weights are designed to reduce the semantic gap between the extracted visual features and the userxe2x80x99s highlevel semantics. We mathematically formulate the proposed solution through the minimization of some objective functions. This optimization aims to produce optimal feature relevance weights with respect to the user query. The proposed approach is assessed using an image collection from the Corel database.