Predicting Image Aesthetics for Intelligent Tourism Information Systems

Abstract

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.

Electronics and Its Worldwide Research

“…By exploiting the automatic reasoning capabilities provided by semantic web technologies and the collection of data through a network of sensors, personalized comfort profiles are sent to guests. This customization is also intended to be extrapolated to other areas such as tourist information [115], or for the development of domestic environment [116].…”

Less-Known Tourist Attraction Discovery Based on Geo-Tagged Photographs

“…[56] ameliorated the model to distinguish image of diabetic retinopathy. To find out guide-suitable pictures for improving the touristic experience, Kleinlein et al [58] presented an approach to classify photographs into three labels base on aesthetic perception. Different from object detection, image classification only can annotate one label for the photograph.…”

Biologically-Inspired Computational Neural Mechanism for Human Action/activity Recognition: A Review

“…This approach extracts the features from spatial and temporal dimensions via a convolutional network, captures the motion information encoded from adjacent frames, and generates and combines multiple channel information. The presented approach has successfully implied a bio-inspired method through CNN and motion information combination for actual environments [99] (Another similar approaches are [116][117][118][119][120][121][122][123][124][125], long-short term memory (LSTM) [98,104,108,126]). Figure 5.…”

“…Deep learning and the biologically inspired mechanism: Finally, in terms of machine learning, another possibility would be to create another machine learning framework (with respect to biological evidences) and modify the system from episodic recognition or the frame recognition with overall understanding of the movements. In this area, with the recent developments in deep learning approaches, this concept is implemented and would be a good methodology in term of involving shape features (from form pathway) and motion information (from motion pathway) (some examples are deep learning applications, which can be justified with biological connection [98,99,104,108,[116][117][118][119][120][121][122][123][124][125][126]). One particular trend is on applying the framework to learn more complexity in biological movements depending on deep learning based machine vision applications.…”