Next Article in Journal
Performance Analysis of Low-Level and High-Level Intuitive Features for Melanoma Detection
Next Article in Special Issue
A Rapid Recognition Method for Electronic Components Based on the Improved YOLO-V3 Network
Previous Article in Journal
Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks
Previous Article in Special Issue
Automatic Tool for Fast Generation of Custom Convolutional Neural Networks Accelerators for FPGA
Open AccessArticle

Predicting Image Aesthetics for Intelligent Tourism Information Systems

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
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;
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
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop