Geo-located image analysis using latent representations

Image categorization is undoubtedly one of the most challenging open problems faced in computer vision, far from being solved by employing pure visual cues. Recently, additional textual ldquotagsrdquo can be associated to images, enriching their semantic interpretation beyond the pure visual aspect, and helping to bridge the so-called semantic gap. One of the latest class of tags consists in geo-location data, containing information about the geographical site where an image has been captured. Such data motivate, if not require, novel strategies to categorize images, and pose new problems to focus on. In this paper, we present a statistical method for geo-located image categorization, in which categories are formed by clustering geographically proximal images with similar visual appearance. The proposed strategy permits also to deal with the geo-recognition problem, i.e., to infer the geographical area depicted by images with no available location information. The method lies in the wide literature on statistical latent representations, in particular, the probabilistic latent semantic analysis (pLSA) paradigm has been extended, introducing a latent aspect which characterizes peculiar visual features of different geographical zones. Experiments on categorization and georecognition have been carried out employing a well-known geographical image repository: results are actually very promising, opening new interesting challenges and applications in this research field.