Attributed Relational SIFT-Based Regions Graph for Art Painting Retrieval

Recently, image retrieval and analysis algorithms have been extensively applied to art related domains. In this field, state-of-the-art approaches mainly focus on feature extraction with the aim of improving reliability of authentication, classification and retrieval of art paintings. In this paper we propose an effective modeling, based on a graph structure, and a retrieval strategy, based on a graph matching algorithm, for art paintings. The proposed approach has been tested on different datasets with high quality results allowing an user to run effective content-based queries on painting records