Analyzing Flickr metadata to extract location-based information and semantically organize its photo content

Spyrou, Evaggelos; Mylonas, Phivos

doi:10.1016/j.neucom.2014.12.104

Cited by 33 publications

(11 citation statements)

References 55 publications

(59 reference statements)

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“…To this goal, many approaches have been proposed in the literature, however it still remains an open challenge, since none could guarantee an optimal solution. When equality of tiles is necessary, overlapping regions may be used (e.g., as in our previous work [36]), or alternatively, simpler, square grid-based regions (e.g., as in [37]). Although overlapping regions have a few advantages, e.g., closely geo-tagged photos belong to the same geo-cluster, this would imply that overlapped tiles would share descriptions (tags).…”

Section: Notation and Definitionsmentioning

confidence: 99%

A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics

et al. 2017

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Living in the "era of social networking", we are experiencing a data revolution, generating an astonishing amount of digital information every single day. Due to this proliferation of data volume, there has been an explosion of new application domains for information mined from social networks. In this paper, we leverage this "socially-generated knowledge" (i.e., user-generated content derived from social networks) towards the detection of areas-of-interest within an urban region. These large and homogeneous areas contain multiple points-of-interest which are of special interest to particular groups of people (e.g., tourists and/or consumers). In order to identify them, we exploit two types of metadata, namely location-based information included within geo-tagged photos that we collect from Flickr, along with plain simple textual information from user-generated tags. We propose an algorithm that divides a predefined geographical area (i.e., the center of Athens, Greece) into "tile"-shaped sub-regions and based on an iterative merging procedure, it aims to detect larger, cohesive areas. We examine the performance of the algorithm both in a qualitative and quantitative manner. Our experiments demonstrate that the proposed geo-clustering algorithm is able to correctly detect regions that contain popular tourist attractions within them with very promising results.

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Section: Notation and Definitionsmentioning

confidence: 99%

A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics

et al. 2017

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“…Then, we divide this region into sub-regions. Many approaches have been proposed for this, e.g., in our previous work [7] we used equally-sized, round, overlapping regions. However, this would imply that overlapped tiles would share descriptions (tags), which is not a desired property in the context of this work.…”

Section: Notation and Definitionsmentioning

confidence: 99%

Discovering Areas of Interest Using a Semantic Geo-Clustering Approach

Spyrou

Psallas

Charalampidis

et al. 2016

IFIP Advances in Information and Communication Technology

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Living in the era of social networking, coupled together with great advances in digital multimedia user-generated content, motivated us to focus our research work on humanistic data generated by such activities towards new, more efficient ways of extracting semantically meaningful information in the process. More specifically, the herein proposed approach aims to extract areas of interest in urban areas, utilizing the increasing socially-generated knowledge from social networks. A part of the area of interest is selected, then split into "tiles" and processed with an iterative merging approach whose goal is to extract larger, "homogeneous" areas which are of special (e.g., tourist) interest. In this work generated areas of interest focus on interesting points from the humanistic point of view, thus covering in general main touristic attractions and places of interest. In order to achieve our goals, we exploit two types of metadata, namely location-based information (geo-tags) geo-tags and simple user-generated tags.

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“…More specifically, we follow the approach we proposed in [15], where we used this clustering scheme in order to characterize the content in terms of its semantics, spatial and chronological context. In this Section we shall initially briefly present the clustering algorithm we adopted and elaborate on its application on the given dataset.…”

Section: Processing Photo Geotagsmentioning

confidence: 99%

“…In previous work [15], we referred to this semantically enhanced level as "places". Now, we select a relatively smaller distortion r 2 : r 2 < r 1 .…”

Section: B Photo Clusteringmentioning

confidence: 99%

Mining tourist routes from Flickr photos

Spyrou¹,

Sofianos²,

Mylonas³

2015

2015 10th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)

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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.

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Analyzing Flickr metadata to extract location-based information and semantically organize its photo content

Cited by 33 publications

References 55 publications

A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics

A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics

Discovering Areas of Interest Using a Semantic Geo-Clustering Approach

Mining tourist routes from Flickr photos

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