Selection of canonical images of travel attractions using image clustering and aesthetics analysis

  • Authors:

  • Jen-Chang Liu;Yin-Chen Liang;Shih-Wei Lin

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Chi Nan University, No. 1, University Rd., Puli, Nantou County, Nantou 545, Taiwan;Department of Computer Science and Information Engineering, National Chi Nan University, No. 1, University Rd., Puli, Nantou County, Nantou 545, Taiwan;Department of Computer Science and Information Engineering, National Chi Nan University, No. 1, University Rd., Puli, Nantou County, Nantou 545, Taiwan

  • Venue:
  • International Journal of Computational Science and Engineering
  • Year:
  • 2013

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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.