Detecting the long-tail of Points of Interest in tagged photo collections

TitleDetecting the long-tail of Points of Interest in tagged photo collections
Publication TypeConference Paper
Year of Publication2011
AuthorsZigkolis, Christos, Symeon Papadopoulos, Yiannis Kompatsiaris, and Athena Vakali
EditorMartinez, José M.
Book TitleCBMI
ISBN Number978-1-61284-433-6

The paper tackles the problem of matching the photosof a tagged photo collection to a list of “long-tail” PointsOf Interest (PoIs), that is PoIs that are not very popularand thus not well represented in the photo collection. Despitethe significance of improving “long-tail” PoI photoretrieval for travel applications, most landmark detectionmethods to date have been tested on very popular landmarks.In this paper, we conduct a thorough empirical analysiscomparing four baseline matching methods that relyon photo metadata, three variants of an approach that usescluster analysis in order to discover PoI-related photo clusters,and a real-world retrieval mechanism (Flickr search)on a set of less popular PoIs.A user-based evaluation of the aforementioned methodsis conducted on a Flickr photo collection of over 100, 000photos from 10 well-known touristic destinations in Greece.A set of 104 “long-tail” PoIs is collected for these destinationsfrom Wikipedia, Wikimapia and OpenStreetMap. Theresults demonstrate that two of the baseline methods outperformFlickr search in terms of precision and F-measure,whereas two of the cluster-based methods outperform it interms of recall and PoI coverage. We consider the results ofthis study valuable for enhancing the indexing of pictorialcontent in social media sites.


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