PUBLIC MARKS with search classification

Estimating geographic information from an image is an excellent, difficult high-level computer vision problem whose time has come. The emergence of vast amounts of geographically-calibrated image data is a great reason for computer vision to start looking globally — on the scale of the entire planet! In this paper, we propose a simple algorithm for estimating a distribution over geographic locations from a single image using a purely data-driven scene matching approach. For this task, we will leverage a dataset of over 6 million GPS-tagged images from the Internet. We represent the estimated image location as a probability distribution over the Earth's surface. We quantitatively evaluate our approach in several geolocation tasks and demonstrate encouraging performance (up to 30 times better than chance). We show that geolocation estimates can provide the basis for numerous other image understanding tasks such as population density estimation, land cover estimation or urban/rural classification.

classification of HTTP-based APIs. The classification achieves an explicit differentiation between the various kinds of uses of HTTP and provides a foundation to analyse and describe the system properties induced.

I have gathered some notes on my use of machine tag for biology taxonomy. So you can express easily the classification on web services using tagging (like flickr or del.icio.us) a machine tag that can be read by human and by machine (read information automated systems) :

It was shown that for best results all the elements have to be included in the classification process. The exact way of combining the significance indicators turned out not to be overly important: using the F1 measure, the best combination of significance indicators yielded no more than 3 % higher performance results than the baseline.