Feature Selection Guided by Perception in Medical CBIR Systems
Pedro H. Bugatti, Marcela Xavier Ribeiro, Agma J. M. Traina, Caetano Traina · 2011
This work aims at developing an efficient support to improve the precision of content-based medical image retrieval systems and also accelerate such retrieval, introducing a novel retrieval approach that integrates techniques of feature selection and relevance feedback to perform feature selection guided by perceptual similarity. Low-level features are commonly employed to represent the images by content. Feature selection is performed employing statistical association rules integrated with a relevance feedback process, tuning the mining process on the fly, according to the user's perception. This integration not only improves the feature selection accuracy, but also allows personalising such process. The experiments performed show that the method improves up to 30% the query precision and decreases up to 11.6 times the number of features employed to compute the similarity in the content-based query, also decreasing the processing costs and memory requirements of the query execution.