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Open AccessArticle

Predicting Image Aesthetics for Intelligent Tourism Information Systems

1
Speech Technology Group, Center for Information Processing and Telecommunications, E.T.S.I. de Telecomunicación, Universidad Politécnica de Madrid, Avda. Complutense 30, 28040 Madrid, Spain
2
Department of Signal Theory and Communications, University of Carlos III de Madrid, 28911 Leganés, Madrid, Spain
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(6), 671; https://doi.org/10.3390/electronics8060671
Received: 13 May 2019 / Revised: 7 June 2019 / Accepted: 11 June 2019 / Published: 13 June 2019
(This article belongs to the Special Issue Deep Neural Networks and Their Applications)
Image perception can vary considerably between subjects, yet some sights are regarded as aesthetically pleasant more often than others due to their specific visual content, this being particularly true in tourism-related applications. We introduce the ESITUR project, oriented towards the development of ’smart tourism’ solutions aimed at improving the touristic experience. The idea is to convert conventional tourist showcases into fully interactive information points accessible from any smartphone, enriched with automatically-extracted contents from the analysis of public photos uploaded to social networks by other visitors. Our baseline, knowledge-driven system reaches a classification accuracy of 64.84 ± 4.22% telling suitable images from unsuitable ones for a tourism guide application. As an alternative we adopt a data-driven Mixture of Experts (MEX) approach, in which multiple learners specialize in partitions of the problem space. In our case, a location tag is attached to every picture providing a criterion to segment the data by, and the MEX model accordingly defined achieves an accuracy of 85.08 ± 2.23%. We conclude ours is a successful approach in environments in which some kind of data segmentation can be applied, such as touristic photographs. View Full-Text
Keywords: computer vision; tourism; domain adaptation; Mixture of Experts; CNN computer vision; tourism; domain adaptation; Mixture of Experts; CNN
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Kleinlein, R.; García-Faura, Á.; Luna Jiménez, C.; Montero, J.M.; Díaz-de-María, F.; Fernández-Martínez, F. Predicting Image Aesthetics for Intelligent Tourism Information Systems. Electronics 2019, 8, 671.

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