Discovering Obscure Sightseeing Spots by Analysis of Geo-tagged Social Images (original) (raw)

Less-Known Tourist Attraction Discovery Based on Geo-Tagged Photographs

Machine Learning and Knowledge Extraction

Most existing studies of tourist attraction recommendations have specifically emphasized analyses of popular sites. However, recommending such spots encourages crowds to flock there in large numbers, making tourists feel uncomfortable. Furthermore, some studies have discovered that quite a few tourists dislike crowded destinations and prefer to avoid them. A ready solution is discovery and publicity of less-known tourist attractions. Especially, this study specifically examines discovery of less-known Japanese tourist destinations that are attractive and merit increased visits. Using this approach, crowds can not only be dispersed from popular tourist attractions, but more diverse spots can be provided for travelers to choose from. By analyzing geo-tagged photographs on Flickr, we propose a formula that incorporates different aspects such as image quality assessment (IQA), comment sentiment, and tourist attraction popularity for ranking tourist attractions. We investigate Taiwanese ...

Place Recommendation with Geo-tagged Photos

2020

We analyse and combine a number of worldwide crowd-sourced geotagged databases with the goal to locate, describe and rate potential tourism targets in any area in the world. In particular, we address the problem of finding representative names and top POIs for popular areas, with the main focus on sightseeing. The results are demonstrated on the sightsmap.com site presenting a zoomable and pannable tourism popularity heat map along with popularity-sorted POI markers for concrete objects.

Exploiting Flickr Tags and Groups for Finding Landmark Photos

2009

Many people take pictures of different city landmarks and post them to photo-sharing systems like Flickr. They also add tags and place photos in Flickr groups, created around particular themes. Using tags, other people can search for representative landmark images of places of interest. Searching for landmarks using tags results into many non-landmark photos and provides poor landmark summary for a city. In this paper we propose a new method to identify landmark photos using tags and social Flickr groups. In contrast to similar modern systems, our approach is also applicable when GPS-coordinates for photos are not available. Presented user study shows that the proposed method outperforms state-of-the-art systems for landmark finding.

A Survey of Geo-tagged Multimedia Content Analysis within Flickr

IFIP Advances in Information and Communication Technology, 2014

Our survey paper attempts to investigate how recent and undoubted emerge in enriched, geo-tagged social networks' multimedia content sharing works to the benefit of their users and whether it could be handled in a formal way, in order to capture the meaningful semantics rising from this newly introduced user experience. It further specializes its focus by providing an overview of current state-of-the-art techniques with respect to geo-tagged content access, processing and manipulation within the popular Flickr social network. In this manner it explores the role of information retrieval, integration and extraction from the technical point of view, coupled together with human social network activities, like, for instance, localization and recommendations based on pre-processed collaborative geo-tagged photos, resulting into more efficient, optimized search results.

Extracting touristic information from online image collections

2012

In this paper, we present a Geographical Information Retrieval system, which aims to automatically extract and analyze touristic information from photos of online image collections (in our case of study Flickr). Our system collect all the photos, and the related information, that are associated to a specific city. We then use Google Maps service to geolocate the retrieved photos, and finally we analyze geo-referenced data to obtain our goals: 1) determining and locating the most interesting places of the city, i.e. the most visited locations, and 2) reconstructing touristic routes of the users visiting the city. Information is filtered by using a set of constraints, which we apply to select only the users that reasonably are tourists visiting the city. Tests were performed on an Italian city, Palermo, that is rich in artistic and touristic attractions, but preliminary tests showed that our technique could successfully be applied to any city in the world with a reasonable number of touristic landmarks.

Sightseeing Value Estimation by Analyzing Geosocial Images

2016 IEEE Second International Conference on Multimedia Big Data (BigMM), 2016

Recommendation of points of interests (POIs) is drawing more attention to meet the growing demands of tourists. Thus, a POI's quality (sightseeing value) needs to be estimated. In contrast to conventional studies that rank POIs on the basis of user behavior analysis, this paper presents methods to estimate quality by analyzing geo-social images. Our approach estimates the sightseeing value from two aspects: (1) nature value and (2) culture value. For the nature value, we extract image features that are related to favorable human perception to verify whether a POI would satisfy tourists in terms of environmental psychology. Three criteria are defined accordingly: coherence, image-ability, and visual-scale. For the culture value, we recognize the main cultural element (i.e., architecture) included in a POI. In the experiments, we applied our methods to real POIs and found that our approach assessed sightseeing value effectively.

Mining tourist routes from Flickr photos

2015

Popular social networking sites like Flickr are nowadays overwhelmed by geo-tagged photos. Semi-automatic discovery of touristic routes and landmarks from such a pool of photos forms a challenging task. In this paper we attempt to analyze user-generated routes within downtown city areas defined around a pre-selected geographical bounding box and derived from a large geo-tagged Flickr dataset, by utilizing a novel two-level clustering scheme. Our goal is to select routes of touristic interest for a given area. Without loss of generality the latter is considered to be a predefined "window" around a city's most famous landmarks and touristic attractions. The herein proposed framework has been applied to a real-life geotagged Flickr photos dataset from a major European metropolis: Athens, Greece.

Using Flickr geotagged photos to estimate visitor trajectories in World Heritage cities

International Journal of Geo-Information, 2020

World tourism dynamics are in constant change, as well as they are deeply shaping the trajectories of cities. The "call effect" for having the World Heritage status has boosted tourism in many cities. The large number of visitors and the side effects, such as the overcrowding of central spaces, are arousing the need to develop and protect heritage assets. Hence, the analysis of tourist spatial behaviour is critical for tackling the needs of touristified cities correctly. In this article, individual visitor spatiotemporal trajectories are reconstructed along with the urban network using thousands of geotagged Flickr photos taken by visitors in the historic centre of the World Heritage City of Toledo (Spain). A process of trajectory reconstruction using advanced GIS techniques has been implemented. The spatial behaviour has been used to classify the tourist sites offered on the city's official tourist map, as well as to identify the association with the land uses. Results bring new knowledge to understand visitor spatial behaviour and new visions about the influence of the urban environment and its uses on the visitor spatial behaviour. Our findings illustrate how tourist attractions and the location of mixed commercial and recreational uses shape the visitor spatial behaviour. Overflowed streets and shadow areas underexplored by visitors are pinpointed.