A Back End System for Digital Image Library Organization Based on Concept Learning

We present a back-end system which organizes digital image libraries according to a user-defined concept. The concept is extracted from a set of images that the user submits to the system as its representative instances. A relevance feedback procedure, implemented with SVM-based incremental learning algorithms, tunes a classifier that discriminates between concept-relevant and concept-irrelevant images. The system is fully developed and experimentally tested with success.