Evaluating boundary conditions and hierarchical visualization in CBIR

Abstract

Multiple descriptors are employed to represent images in Content-Based Image Retrieval (CBIR) systems. Each descriptor consists of a feature extractor associated with a distance function. An extractor is generally suitable for representing a specific subset of images on a database. The boundary conditions are information used to detect this subset. The use of visualization in CBIR helps to represent the similarity relationship between images, improving the user's understanding of the CBIR system, allowing them to modify parameters to obtain better results. It is proven that the use of multiple descriptors with boundary conditions tends to improve the precision of CBIR queries, but there is no data on the impact that the technique generates on visualization. This paper uses multiple descriptors with boundary conditions to generate a Neighbor Joining similarity tree-based view. Tests have shown that the quality of the visualization may be related to the quality of the query result. In the context of similarity trees, the use of multiple descriptors contributed to a better-organized visualization.