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 1.06 million street-view images and the MediaEval'15 Placing Task dataset with 5.6 million geo-tagged images from Flickr. We present examples that illustrate the highly transparent mechanics of the approach, which are based on commonsense 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.

Today's geo-location estimation approaches are able to infer the location of a target image using its visual content alone. These approaches typically exploit visual matching techniques, applied to a large collection of background images with known geo-locations. Users who are unaware that visual analysis and retrieval approaches can compromise their geo-privacy, unwittingly open themselves to risks of crime or other unintended consequences. This paper lays the groundwork for a new approach to geo-privacy of social images: Instead of requiring a change of user behavior, we start by investigating users' existing photo-sharing practices. We carry out a series of experiments using a large collection of social images (8.5M) to systematically analyze how photo editing practices impact the performance of geo-location estimation. We find that standard image enhancements, including filters and cropping, already serve as natural geo-privacy protectors. In our experiments, up to 19% of images whose location would otherwise be automatically predictable were unlocalizeable after enhancement. We conclude that it would be wrong to assume that geo-visual privacy is a lost cause in today's world of rapidly maturing machine learning. Instead, protecting users against the unwanted effects of pixel-based inference is a viable research field. A starting point is understanding the geo-privacy bonus of already established user behavior.

Spatial verification is a key step in boosting the performance of object-based image retrieval. It serves to eliminate unreliable correspondences between salient points in a given pair of images, and is typically performed by analyzing the consistency of spatial transformations between the image regions involved in individual correspondences. In this paper, we consider the pairwise geometric relations between correspondences and propose a strategy to incorporate these relations at significantly reduced computational cost, which makes it suitable for large-scale object retrieval. In addition, we combine the information on geometric relations from both the individual correspondences and pairs of correspondences to further improve the verification accuracy. Experimental results on three reference datasets show that the proposed approach results in a substantial performance improvement compared to the existing methods, without making concessions regarding computational efficiency.

We propose an automatic method that addresses the challenge of predicting the geo-location of social images using only the visual content of those images. Our method is able to generate a geo-location prediction for an image globally . In this respect, it contrasts with other existing approaches, specifically with those that generate predictions restricted to specific cities, landmarks, or an otherwise pre-defined set of locations. The essence and the main novelty of our ranking-based method is that for a given query image a geo-location is recommended based on the evidence collected from images that are not only geographically close to this geo-location, but also have sufficient visual similarity to the query image within the considered image collection. Our method is evaluated experimentally on a public dataset of 8.8 million geo-tagged images from Flickr, released by the MediaEval 2013 evaluation benchmark. Experiments show that the proposed method delivers a substantial performance improvement compared to the existing related approaches, particularly for queries with high numbers of neighbors . In addition, a detailed analysis of the method's performance reveals the impact of different visual feature extraction and image matching strategies, as well as the densities and types of images found at different locations, on the prediction accuracy.

Predicting geographic location using exclusively the visual content of images holds the promise of greatly benefiting users' access to media collections. In this paper, we present a visual-content-based approach that predicts where in the world a social image was taken. We employ a ranking method that assigns a query photo the geo-location of its most likely geo-visual neighbor in the social image collection. The novelty of the approach is that ranking makes use not only of the photos themselves, but also their geo-visual neighbors. In contrast to other approaches, we do not restrict the locations we predict to landmarks or specific cities. The approach is evaluated on a set of 3 million geo-tagged photos from Flickr, released by MediaEval 2012. Experiments show that the proposed system delivers a substantive performance improvement compared with previously proposed, related visual content-based approaches. The discussion illustrates how photo densities, geo-visual redundancy and uploader patterns characteristic of social image collections impacts the performance.

Quantisation index modulation (QIM) is an important class of watermarking methods, which has been widely used in blind watermarking applications. It is well known that spread transform dither modulation (STDM), as an extension of QIM, has good performance in robustness against random noise and re-quantisation. However, the quantisation step-sizes used in STDM are random numbers not taking features of the image into account. The authors present a step projection-based approach to incorporate the perceptual model with STDM framework. Four implementations of the proposed algorithm are further presented according to different modified versions of the perceptual model. Experimental results indicate that the step projection-based approach can incorporate the perceptual model with STDM framework in a better way, thereby providing a significant improvement in image fidelity. Compared with the former proposed modified schemes of STDM, the author's best performed implementation provides powerful resistance against common attacks, especially in robustness against Gauss noise, salt and pepper noise and JPEG compression.