Abstract
Photos have been an important means to transfer destination image to potential travelers, as they are widely available on travel related web sites. However, there has been very limited attempt to assess visual quality of online photos, which plays an important role in influencing traveler emotion and their travel intention. This is probably due to the limited background in photography among tourism researchers, and the inefficiency of manual assessment approach. Aiming to overcome these barriers, this paper presents a computational approach to visual features extraction for automatic photo quality assessment. We describe a number of visual features, which are helpful in reflecting the photo quality, and then validate their performance through a number of experiments using a large-scale data set of online travel photos. The introduced approach has the potential to facilitate the evaluation of visual quality of photos on travel web sites and online travel photos.
Keywords
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Acharya, T., Ray, A.K.: Image Processing: Principles and Applications. Wiley, Milton (2005)
Bai, B., Law, R., Wen, I.: The impact of website quality on customer satisfaction and purchase intentions: evidence from Chinese online visitors. Int. J. Hosp. Manag. 27(3), 391–402 (2008)
Blanchet, G., Moisan, L.: An explicit sharpness index related to global phase coherence. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1065–1068. Kyoto, Japan (2012)
Buhalis, D., Law, R.: Progress in information technology and tourism management: 20 years on and 10 years after the Internet: the state of e-Tourism research. Tour. Manag. 29(4), 609–623 (2008)
Caviedes, J., Gurbuz, S.: No-reference sharpness metric based on local edge kurtosis. In: Proceedings of the International Conference on Image Processing, vol. 3, pp. III-53–III-56. New York, USA (2002)
Chen, M.-J., Bovik, A.C.: No-reference image blur assessment using multiscale gradient. In: Proceedings of the International Workshop on Quality of Multimedia Experience, pp. 70–74. San Diego, CA (2009)
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Studying aesthetics in photographic image using a computational approach. In: Proceedings of the 9th European Conference on Computer Vision, pp. 288–301. Graz, Austria (2006)
Ert, E., Fleischer, A., Magen, N.: Trust and reputation in the sharing economy: the role of personal photos in airbnb. Tour. Manag. 55, 62–73 (2016)
Garrod, B.: Understanding the relationship between tourism destination imagery and tourist photography. J. Travel Res. 47(3), 346–358 (2009)
Govers, R., Go, F.M., Kumar, K.: Promoting tourism destination image. J. Travel Res. 46(1), 15–23 (2007)
Hao, X., Wu, B., Morrison, A.M., Wang, F.: Worth thousands of words? visual content analysis and photo interpretation of an outdoor tourism spectacular performance in Yangshuo Guilin, China. Anatolia 27(2), 201–213 (2016)
Hasler, D., Suesstrunk, E.S.: Measuring colourfulness in natural images. In: Proceedings of the SPIE, vol. 5007, pp. 87–95 (2003)
ITU: Bt.601: studio encoding parameters of digital television for standard 4:3 and wide screen 16:9 aspect ratios. International Telecommunication Union. Retrieved on 5 Mar 2017, from http://www.itu.int/rec/R-REC-BT.601/ (2011)
Jeong, M., Choi, J.: Effects of picture presentations on customers’ behavioral intentions on the web. J. Travel Tour. Market. 17(2–3), 193–204 (2005)
Ke, Y., Tang, X., Jing, F.: The design of high-level features for photo quality assessment. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 419–426. New York (2006)
Law, R., Qi, S., Buhalis, D.: Progress in tourism management: a review of website evaluation in tourism research. Tour. Manag. 31(3), 297–313 (2010)
Leung, X.Y., Bai, B.: How motivation, opportunity, and ability impact travelers’ social media involvement and revisit intention. J. Travel Tour. Market. 30(1–2), 58–77 (2013)
Li, G., Law, R., Vu, H.Q., Rong, J.: Discovering the hotel selection preferences of Hong Kong inbound travelers using Choquet integral. Tour. Manag. 36, 321–330 (2013)
Li, G., Law, R., Vu, H.Q., Rong, J., Zhao, X.: Identifying emerging hotel preferences using emerging pattern mining technique. Tour. Manag. 46, 311–321 (2015)
Liu, C., Arnett, K.P., Litecky, C.: Design quality of websites for electronic commerce: fortune 1000 webmasters evaluations. Electron. Markets 10(2), 120–129 (2000)
Liu, X., Tanaka, M., Okutomi, M.: Single-image noise level estimation for blind denoising. IEEE. T. Image. Process. 22(12), (2013)
Marchesotti, L., Perronnin, F., Larlus, D., Csurka, G.: Assessing the aesthetic quality of photographs using generic image descriptors. In: Proceedings of the 2011 International Conference on Computer Vision, pp. 1784–1791. Barcelona, Spain (2011)
Molina, A., Esteban, A.: Tourism brochures usefulness and image. Ann. Tour. Res. 33(4), 1036–1056 (2006)
National Instrument: Colour image representations. Retrieved on 5 Jan 2017, from http://www.ni.com/white-paper/2723/en/ (2016)
Pan, S., Lee, J., Tsai, H.: Travel photos: motivations, image dimensions, and affective qualities of places. Tour. Manag. 40, 59–69 (2014)
Pedro, J.S., Siersdorfer, S.: Ranking and classifying attractiveness of photos in folksonomies. In: Proceedings of the 18th International Conference on World Wide Web, pp. 771–780. Madrid, Spain (2009)
Phelan, K.V., Christodoulidou, N., Countryman, C.C., Kistner, L.J.: To book or not to book: the role of hotel web site heuristics. J. Serv. Mark. 25(2), 134–148 (2011)
Savakis, E.A., Etz, P.S., Loui, C.A.: Evaluation of image appeal in consumer photography. SPIE Proc. High. Level Process. Image Qual. 3959, 111–120 (2000)
Shapiro, L.G., Stockman, G.C.: Computer Vision. Prentice Hall, Upper Saddle River (2001)
Sivaji, A., Tzuaan, S.S., Yang, L.T., Russin, M.B.A.: Hotel photo gallery and Malaysian travelers: preliminary findings. In: Proceedings of the 2014 3rd International Conference on User Science and Engineering (i-USEr), pp. 258–263. Shah Alam, Malaysia (2014)
Song, S.-G., Kim, D.-Y.: A pictorial analysis of destination images on Pinterest: the case of tokyo, kyoto, and osaka, japan. J. Travel Tour. Mark. 33(5), 687–701 (2016)
Stepchenkova, S., Zhan, F.: Visual destination images of Peru: comparative content analysis of DMO and user-generated photography. Tour. Manag. 36, 590–601 (2013)
Stokes, M., Anderson, M., Chandrasekar, S., Motta, R.: A standard default colour space for the internet—sRGB. Retrieved 10 Jan 2017, from https://www.w3.org/Graphics/Colour/sRGB.html (1996)
Stringam, B.B., Gerdes Jr., J.: Are pictures worth a thousand room nights? Success factors for hotel web site design. J. Hosp. Tour. Technol. 1(1), 30–49 (2010)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison Wesley, New York (2006)
Valdez, P., Mehrabian, A.: Effects of colour on emotions. J. Exp. Psychol. Gen. 123(4), 394–409 (1994)
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Trpkovski, A., Vu, H.Q., Li, G., Wang, H., Law, R. (2018). Automatic Hotel Photo Quality Assessment Based on Visual Features. In: Stangl, B., Pesonen, J. (eds) Information and Communication Technologies in Tourism 2018. Springer, Cham. https://doi.org/10.1007/978-3-319-72923-7_30
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DOI: https://doi.org/10.1007/978-3-319-72923-7_30
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