Adaptive features selection for expert datasets: A cultural heritage application

Image Retrieval is still a very active field of image processing as the number of available image datasets continuously increases. One of the principal objectives of Content-Based Image Retrieval (CBIR) is to return to user the most similar images to a given query with respect to their visual content. Our work fits in a very specific application context: indexing small expert image dataset, e.g. cultural heritage images, with no prior knowledge on the images. Because of the image complexity, one of our contributions is the choice of effective descriptors from literature placed in direct competition. Two strategies are used to combine features: a psycho-visual one and a statistical one. In this context, we propose an automatic and adaptive framework based on the well-known bags of visual words and phrases models that select relevant visual descriptors for each keypoint to construct a more discriminative image representation. Experiment results show the adaptiveness and the performance of our framework on “generic” benchmark datasets and on two cultural heritage datasets.