Author： Liu Jen-Chang Liang Yin-Chen Lin Shih-Wei
Publisher： Inderscience Publishers
ISSN： 1742-7185
Source： International Journal of Computational Science and Engineering, Vol.8, Iss.4, 2013-10, pp. : 324-335
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Abstract
The popularity of smart phones and development of 3G mobile networks have dramatically increased the sharing of photos on social platforms. However, the huge numbers of images depicting tourist attractions uploaded to photo-sharing websites vary in terms of subjective photographic intent and contain considerable environmental noise. We propose an approach for finding canonical images of travel attractions from online social platforms, and apply aesthetics analysis to rank the results. This approach can provide travel websites with a mechanism for automatic image selection and help travellers browse travel spots. The methods used include face detection to filter attractions images containing people, feature extraction, and feature classification to filter out background features. We then calculate the similarity among images, and apply an affinity propagation algorithm for clustering and find canonical images. Finally, the clustered images are ranked by aesthetics scores. Experimental results show that the proposed approach obtains representative and aesthetically-pleasing images for attractions that include artificial landmarks.
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