@inproceedings {conf/cbmi/ZigkolisPKV11, title = {Detecting the long-tail of Points of Interest in tagged photo collections}, booktitle = {CBMI}, year = {2011}, pages = {235-240}, publisher = {IEEE}, organization = {IEEE}, abstract = {

The paper tackles the problem of matching the photosof a tagged photo collection to a list of {\^a}{\texteuro}{\'s}long-tail{\^a}{\texteuro}{\v t} PointsOf Interest (PoIs), that is PoIs that are not very popularand thus not well represented in the photo collection. Despitethe significance of improving {\^a}{\texteuro}{\'s}long-tail{\^a}{\texteuro}{\v t} 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 {\^a}{\texteuro}{\'s}long-tail{\^a}{\texteuro}{\v t} 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.

}, isbn = {978-1-61284-433-6}, author = {Christos Zigkolis and Symeon Papadopoulos and Yiannis Kompatsiaris and Athena Vakali}, editor = {Martinez, Jos{\'e} M.} }