Image categorization is undoubtedly one of the most challenging problems faced in Computer Vision. The related literature is plenty of methods dedicated to specific classes of images; further, commercial systems are also going to be advertised in the market. Nowadays, additional data can also be associated to the images, enriching its semantic interpretation beyond the pure appearance. This is the case of geo-location data, that contain information about the geographical place where an image has been captured. This data allow, if not require, a different management of the images, for instance, to the purpose of easy retrieval and visualization from a geo-referenced image repository. This paper constitutes a first step in this sense, presenting a method for geo-referenced image categorization. The solution presented here places in the wide literature on the statistical latent descriptions, where the probabilistic Latent Semantic Analysis (pLSA) is one of the most known representative. In particular, we extend the pLS A paradigm, introducing a latent variable modelling the geographical area in which an image has been captured. In this way, we are able to describe the entire image data-set grouping effectively proximal images with similar appearance. Experiments on categorization 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.
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