2 research outputs found
Location Prediction of Social Images via Generative Model
The vast amount of geo-tagged social images has attracted great attention in
research of predicting location using the plentiful content of images, such as
visual content and textual description. Most of the existing researches use the
text-based or vision-based method to predict location. There still exists a
problem: how to effectively exploit the correlation between different types of
content as well as their geographical distributions for location prediction. In
this paper, we propose to predict image location by learning the latent
relation between geographical location and multiple types of image content. In
particularly, we propose a geographical topic model GTMI (geographical topic
model of social image) to integrate multiple types of image content as well as
the geographical distributions, In GTMI, image topic is modeled on both text
vocabulary and visual feature. Each region has its own distribution over topics
and hence has its own language model and vision pattern. The location of a new
image is estimated based on the joint probability of image content and
similarity measure on topic distribution between images. Experiment results
demonstrate the performance of location prediction based on GTMI.Comment: 8 page
Geo-distinctive Visual Element Matching for Location Estimation of Images
We propose an image representation and matching approach that substantially
improves visual-based location estimation for images. The main novelty of the
approach, called distinctive visual element matching (DVEM), is its use of
representations that are specific to the query image whose location is being
predicted. These representations are based on visual element clouds, which
robustly capture the connection between the query and visual evidence from
candidate locations. We then maximize the influence of visual elements that are
geo-distinctive because they do not occur in images taken at many other
locations. We carry out experiments and analysis for both geo-constrained and
geo-unconstrained location estimation cases using two large-scale,
publicly-available datasets: the San Francisco Landmark dataset with
million street-view images and the MediaEval '15 Placing Task dataset with
million geo-tagged images from Flickr. We present examples that
illustrate the highly-transparent mechanics of the approach, which are based on
common sense observations about the visual patterns in image collections. Our
results show that the proposed method delivers a considerable performance
improvement compared to the state of the art