Spatial pyramids for boosting global features in content based image retrieval

Image retrieval deals with the problem of finding relevant images to satisfy a specific user need. Many methods for content based image retrieval have been developed over the years, ranging from global to local features and, lately, to convolutional neural networks. Each of the approaches has its own benefits and drawbacks, but they also have similarities. In this paper we investigate how a method initially developed for local features, pyramid matching, then employed on texture features, spatial pyramids, can enhance general global features. We apply a spatial pyramid based approach to add spatial information to well known and established global descriptors, and present the results of an extensive evaluation that shows that this combination is able to outperform the original versions of the global features.