Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A visual analysis of the relationship between word concepts and geographical locations
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PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
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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 amethod 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 pLSAparadigm, 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.