In the last few years, the query-by-visual-example paradigm gained popularity, specially for content based retrieval systems. As sketches represent a natural way of expressing a synthetic query, recent research efforts focused on developing algorithmic solutions to address the sketch-based image retrieval (SBIR) problem. Within this context, we propose a novel approach for SBIR that, unlike previous methods, is able to exploit the visual complexity inherently present in sketches and images. We introduce academic learning, a paradigm in which the sample learning order is constructed both from the data, as in self-paced learning, and from partial curricula. We propose an instantiation of this paradigm within the framework of coupled dictionary learning to address the SBIR task. We also present an efficient algorithm to learn the dictionaries and the codes, and to pace the learning combining the reconstruction error, the prior knowledge suggested by the partial curricula and the cross-domain code coherence. In order to evaluate the proposed approach, we report an extensive experimental validation showing that the proposed method outperforms the state-of-the-art in coupled dictionary learning and in SBIR on three different publicly available datasets.
['Dan Xu', 'Xavier Alameda-Pineda', 'Jingkuan Song', 'Elisa Ricci', 'Nicu Sebe']
Academic Coupled Dictionary Learning for Sketch-based Image Retrieval