Global-Scale Location Prediction for Social Images Using Geo-Visual Ranking

Li, Xinchao; Larson, Martha; Hanjalic, Alan

doi:10.1109/tmm.2015.2413351

Cited by 11 publications

(25 citation statements)

References 36 publications

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“…Eventually, the cluster of the highest aggregate similarity will be selected, and the centroid location of the cluster will be adopted as the location estimate. One weakness of clustering-dependent approaches such as [2,7] is that the size of the cluster plays an important role, where in some cases, a smaller cluster is likely to preserve a better location estimate for the query image.…”

Section: Retrieval Using Hand-crafted Featuresmentioning

confidence: 99%

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Looking around in the neighbourhood: Location estimation of outdoor urban images

Huang

2021

IET Image Processing

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Visual geolocalisation has remained as a challenge in the research community: Given a query image, and a geo-tagged reference database, the goal is to derive a location estimate for the query image. We propose an approach to tackling the geolocalisation problem in a four-step manner. Essentially, our approach focuses on re-ranking the candidate images after image retrieval, by considering the visual similarity of the candidate and its neighbouring images, to the query image. By introducing the neighbouring images, the visual information of a candidate location has been enriched. The evaluation has been conducted on three street view datasets, where our approach outperforms three baseline approaches, in terms of location estimation accuracy on two datasets. We provide discussions related to, firstly, whether using deep features for image retrieval helps improve location estimation accuracy, and the effectiveness of geographical neighbourhoods; secondly, using different deep architectures for feature extraction, and its impact on estimation accuracy; thirdly, investigating if our approach consistently outperforms the classic 1-NN approach, on two datasets with significant difference in visual elements. INTRODUCTIONHumans are able to recognize places and infer locations from images: Provided with a photo of the Ruins of St. Paul's, some may instantly know it is a landmark in Macau, China. A more challenging example would be an image of coconut trees by the sea. In this case, we may have trouble deriving an accurate location estimate from this generic photo. However, we know this photo depicts "somewhere in the tropical areas". And this knowledge helps narrow down the list of candidate locations. Data association [1] and semantic reasoning both play an essential role in the human ability of visual place recognition [2]. Hays and Efros pointed out that semantic reasoning is a huge challenge computationally in 2008 [2]. Luckily, large image collections available on the Internet have paved the way for datadriven approaches to visual geolocalisation. Many research efforts have been devoted to solving this problem, for prospective applications in robot vision [3], landmark identification [4], as well as city reconstruction [5]. In this article, we confine ourselves to a more defined problem: Given a query image, can we derive a location estimate of the query,This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Retrieval Using Hand-crafted Featuresmentioning

confidence: 99%

“…However, a landmark photo of the Eiffel Tower would make it easier for location estimate. Research work has contributed to the progress in geolocalisation of social network images [2,6,7], and street views [4,8,9] in particular. Second, the geographical distribution of reference images.…”

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confidence: 99%

Looking around in the neighbourhood: Location estimation of outdoor urban images

Huang

2021

IET Image Processing

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“…These tendencies can be considered to be reflections of the common sense expectation that the number of ways in which a query can overlap with true-location images is much larger than the number of ways in which a query can overlap with wrong-location images. Connection with search-based geo-location estimation Next we turn to describe how DVEM extends our general geo-visual ranking (GVR) framework [22]. As previously mentioned, DVEM contributes to the processing step in a search-based Here, we provide a brief review of the functioning of GVR.…”

Section: Rationale and Contributionmentioning

confidence: 99%

Geo-Distinctive Visual Element Matching for Location Estimation of Images

Larson

Hanjalic

2018

IEEE Trans. Multimedia

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Abstract-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 geotagged 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.

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“…Hays and Efros [6] estimated the geographical location of a query photo based on a data-driven scene-matching approach. Li et al [8] improved the scene-matching approach by jointly considering visual similarity and geographical proximity to build a ranking method. Lin et al [7] greatly extended the scene-matching approach by further considering overhead appearance and land cover survey data.…”

Section: Related Workmentioning

confidence: 99%

Weather-Adaptive Distance Metric for Landmark Image Classification

Ding-Shiuan

Chu

2015

Lecture Notes in Computer Science

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Visual appearance of landmark photos changes significantly in different weather conditions. In this work, we obtain weather information from a weather forecast website based on a landmark photo's geotag and taken time information. With weather information, we adaptively adjust weightings for combining distances obtained based on different features and thus propose a weather-adaptive distance measure for landmark photo classification. We verify the effectiveness of this idea, and accomplish one of the early attempts to develop a landmark photo classification system that resists to weather changes.

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Global-Scale Location Prediction for Social Images Using Geo-Visual Ranking

Cited by 11 publications

References 36 publications

Looking around in the neighbourhood: Location estimation of outdoor urban images

Looking around in the neighbourhood: Location estimation of outdoor urban images

Geo-Distinctive Visual Element Matching for Location Estimation of Images

Weather-Adaptive Distance Metric for Landmark Image Classification

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