Unsupervised scaling of multi-descriptor similarity functions for medical image datasets

Content-based search has proven to be a proper complement to textual queries over medical image databases. In many applications, employing multiple image descriptors and combining the respective distance functions using adequate scale factors improves the retrieval accuracy. However, the existing weighting methods are either exhaustive or supervised. In this paper, we present the Fractal-scaled Product Metric, an unsupervised method to determine a scale factor among features in multi-descriptor image similarity assessment based on the Fractal Theory. The composite distance function obtained is not limited to dimensional image descriptors and enables using scalable indexing structures. Experiments have shown that the proposed method determines near-optimal scale factors for the descriptors involved, and always improves the precision of the results, outperforming the individual descriptors up to 31% on the average precision.