Using non-parametric quantum theory to rank images
Songhao Zhu, Baoyun Wang, Yuncai Liu · 2012
Recently learning to rank has become one of the popular means to create a ranking model for social image search. However, the results of existing approaches are not as satisfactory for the large gap between low-level visual features and high-level semantic concepts, and these sophisticated approaches require a significant amount of parameters tuning to be effective and efficient. In this paper, we propose a novel framework for social image re-ranking based on a non-parametric quantum technique, which reranks top retrieved images by considering the interrelationship between images through the quantum estimation and requires no explicit parameter tuning. The basic idea of the proposed framework is inspired by the photon polarization experiment supporting the theory of quantum measurement. Experimental results conducted on the Flickr dataset demonstrate the effectiveness and efficiency of the proposed framework.