References

• 1. 1)
     • 39. Orr, M.: ‘Introduction to radial basis function networks’. Technical report, Technical Report 4/96, Center for Cognitive Science, University of Edinburgh, 1996.
  2. 2)
     • 2. Isola, P., Parikh, D., Torralba, A., et al: ‘Understanding the intrinsic memorability of image’. Advances in Neural Information Processing Systems, Granada, Spain, December 2011, pp. 2429–2437.
  3. 3)
     • 3. Khosla, A., Xiao, J., Torralba, A., et al: ‘Memorability of image regions’. Advances in Neural Information Processing Systems, Lake Tahoe, Nevada, United States, December 2012, vol. 25, pp. 305–313.
  4. 4)
     • 31. ‘Digital photography school, how to use leading lines for better composition’. Available at http://digital-photography-school.com/, accessed April 2016.
  5. 5)
     • 32. Datta, R., Joshi, D., Li, J., et al: ‘Studying aesthetics in photographic images using a computational approach’. 9th European Conf. Computer Vision, Graz, Austria, May 2006, pp. 288–301.
  6. 6)
     • 8. Wang, W., Sun, J., Li, J., et al: ‘Investigation on the influence of visual attention on image memorability’. Image and Graphics – 8th Int. Conf., Tianjin, China, August 2015, pp. 573–582.
  7. 7)
     • 38. Cortes, C., Vapnik, V.: ‘Support-vector networks’, Mach. Learn., 1995, 20, (3), pp. 273–297.
  8. 8)
     • 16. Aydn, T., Smolic, A., Gross, M.: ‘Automated aesthetic analysis of photographic images’, IEEE Trans. Vis. Comput. Graph., 2015, 21, (1), pp. 31–42.
  9. 9)
     • 17. Lo, K.-Y., Liu, K.-H., Chen, C.: ‘Intelligent photographing interface with on-device aesthetic quality assessment’. Computer Vision – ACCV Workshops, Daejeon, Korea, November 2012, pp. 533–544.
  10. 10)
      • 19. Bora, D., Gupta, A., Khan, F.: ‘Comparing the performance of L*A*B* and HSV color spaces with respect to color image segmentation’, CoRR, abs/1506.01472, 2015, pp. 192–203.
  11. 11)
      • 10. Bylinskii, Z., Isola, P., Bainbridge, C., et al: ‘Intrinsic and extrinsic effects on image memorability’, Vis. Res., 2015, 116, pp. 165–178.
  12. 12)
      • 26. Minhas, R., Mohammed, A., Wu, Q.: ‘An efficient algorithm for focus measure computation in constant time’, IEEE Trans. Circuits Syst. Video Technol., 2012, 22, (1), pp. 152–156.
  13. 13)
      • 11. Peng, H., Li, K., Li, B., et al: ‘Predicting image memorability by multi-view adaptive regression’. 23rd ACM Int. Conf. Multimedia Proc., Brisbane, Australia, October 2015, pp. 1147–1150.
  14. 14)
      • 7. Celikkale, B., Erdem, A., Erdem, E.: ‘Visual attention-driven spatial pooling for image memorability’. IEEE Computer Vision and Pattern Recognition Workshops, Portland, OR, USA, June 2013, pp. 976–983.
  15. 15)
      • 47. ‘Amazon Mechanical Turk’. Available at https://www.mturk.com/mturk/welcome/, accessed June 2017.
  16. 16)
      • 41. Wang, Y., Witten, I.H.: ‘Induction of model trees for predicting continuous classes’. Proc. Poster Papers of the European Conf. Machine Learning, University of Economics, Faculty of Informatics and Statistics, Prague, 1997.
  17. 17)
      • 24. Yang, G., Nelson, B.: ‘Wavelet-based auto focusing and unsupervised segmentation of microscopic images’. Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, Las Vegas, Nevada, USAOctober 2003, vol. 3, pp. 2143–2148.
  18. 18)
      • 22. Crete, F., Dolmiere, T., Ladret, P., et al: ‘The blur effect: perception and estimation with a new no-reference perceptual blur metric’. Conf. Human Vision and Electronic Imaging XII, San Jose, CA, USA, January–February 2007, p. 64920I.
  19. 19)
      • 48. Kohavi, R.: ‘A study of cross-validation and bootstrap for accuracy estimation and model selection’. Proc. Fourteenth Int. Joint Conf. Artificial Intelligence (IJCAI), Montréal, Québec, Canada, August 1995, pp. 1137–1145.
  20. 20)
      • 12. Khosla, A., Raju, A., Torralba, A., et al: ‘Understanding and predicting image memorability at a large scale’. IEEE Int. Conf. Computer Vision, Santiago, Chile, December 2015, pp. 2390–2398.
  21. 21)
      • 1. Isola, P., Xiao, J., Parikh, D., et al: ‘What makes a photograph memorable?’, IEEE Trans. Pattern Anal. Mach. Intell., 2014, 36, (7), pp. 1469–1482.
  22. 22)
      • 46. Ramanathan, S., Katti, H., Sebe, N., et al: ‘An eye fixation database for saliency detection in images’. 11th European Conf. Computer Vision, Heraklion, Crete, Greece, September 2010, pp. 30–43.
  23. 23)
      • 21. Schanda, J.: ‘CIE colorimetry’, in Schanda, J. (Ed.) ‘Colorimetry: understanding the CIE system’ (John Wiley & Sons, Hoboken, NJ, 2007), pp. 25–78.
  24. 24)
      • 4. Mancas, M., Le Meur, O.: ‘Memorability of natural scene: the role of attention’. Proc. IEEE Int. Conf. Image Process., Melbourne, Australia, September 2013, pp. 196–200.
  25. 25)
      • 42. Quinlan, R.: ‘Learning with continuous classes’. Proc. 5th Australian Joint Conf. Artificial Intelligence, Hobart, Tasmania, November 1992, pp. 343–348.
  26. 26)
      • 5. Redies, C.: ‘A universal model of esthetic perception based on the sensory coding of natural stimuli’, Spat. Vis., 2007, 21, (1–2), pp. 97–117.
  27. 27)
      • 37. Fayyad, U., Shapiro, G.P., Smyth, P.: ‘From data mining to knowledge discovery: an overview’, Adv. Knowl. Discov. Data Min., 1996, pp. 1–34.
  28. 28)
      • 35. Dhar, S., Ordonez, V., Berg, T.: ‘High level describable attributes for predicting aesthetics and interestingness’. Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA, June 2011, pp. 1657–1664.
  29. 29)
      • 14. Borkin, M., Azalea, A.V., Bylinskii, Z., et al: ‘What makes a visualization memorable?’, IEEE Trans. Vis. Comput. Graph., 2013, 19, (12), pp. 2306–2315.
  30. 30)
      • 23. Pertuz, S., Puig, D., Garc, M.A.: ‘Analysis of focus measure operators for shape-from-focus’, Pattern Recognit., 2013, 46, (5), pp. 1415–1432.
  31. 31)
      • 18. Ng, W.-S., Kao, H.-C., Yeh, C.-H., et al: ‘Automatic photo ranking based on esthetics rules of photography’. Technical report, National Chengchi University, Taipei, Taiwan, 2009.
  32. 32)
      • 27. Mai, L., Le, H., Niu, Y., et al: ‘Detecting rule of simplicity from photos’. ACM Multimedia, New York, NY, USA, October–November 2012, pp. 1149–1152.
  33. 33)
      • 34. Ballard, D.: ‘Generalizing the Hough transform to detect arbitrary shapes’, Pattern Recognit., 1981, 13, (2), pp. 111–122.
  34. 34)
      • 6. Liu, L., Chen, R., Wolf, L., et al: ‘Optimizing photo composition’, Comput. Graph. Forum, 2010, 29, (2), pp. 469–478.
  35. 35)
      • 9. Kim, J., Yoon, S., Pavlovic, V.: ‘Relative spatial features for image memorability’. 21st ACM Int. Conf. Multimedia Proc., Barcelona, Spain, October 2013, pp. 761–764.
  36. 36)
      • 30. Loy, G., Eklundh, O.: ‘Detecting symmetry and symmetric constellations of features’. 9th European Conf. Computer Vision, Graz, Austria, May 2006, pp. 508–521.
  37. 37)
      • 43. Bouckaert, R., Frank, E., Hall, M., et al: ‘Weka manual for version 3-7-13’. Technical report, The University of Waikato, 2015.
  38. 38)
      • 13. Lahrache, S., El Ouazzani, R., El Qadi, A.: ‘Bag-of-features for image memorability evaluation’, IET Comput. Vis., 2016, 10, (6), pp. 1–9.
  39. 39)
      • 36. Mai, L., Le, H., Niu, Y., et al: ‘Rule of thirds detection from photograph’. IEEE Int. Symp. Multimedia, CA, USA, December 2011, pp. 91–96.
  40. 40)
      • 44. Xiao, J., Hayes, J., Ehinger, K., et al: ‘Sun database: large-scale scene recognition from abbey to zoo’. Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA, June 2010, pp. 3485–3492.
  41. 41)
      • 33. Matas, J., Galambos, C., Kittler, J.: ‘Robust detection of lines using the progressive probabilistic Hough transform’, Comput. Vis. Image Underst., 2000, 78, (1), pp. 119–137.
  42. 42)
      • 45. Murray, N., Marchesotti, L., Perronnin, F.: ‘AVA: a large scale database for aesthetic visual analysis’. Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA, June 2012, pp. 2408–2415.
  43. 43)
      • 15. Han, J., Chen, C., Shao, L., et al: ‘Learning computational models of video memorability from fMRI brain imaging’, IEEE Trans. Cybern., 2015, 45, (8), pp. 1692–1703.
  44. 44)
      • 25. Thelen, A., Frey, S., Hirsch, S., et al: ‘Improvements in shape-from-focus for holographic reconstructions with regard to focus operators, neighborhood size, and height value interpolation’, IEEE Trans. Image Process., 2009, 18, (1), pp. 151–157.
  45. 45)
      • 20. Gao, X., Xin, J., Sato, T., et al: ‘Analysis of cross-cultural color emotion’, Color Res. Appl., 2007, 32, (3), pp. 223–229.
  46. 46)
      • 28. Healey, C., Enns, J.: ‘Attention and visual memory in visualization and computer graphics’, IEEE Trans. Vis. Comput. Graph., 2012, 18, (7), pp. 1170–1188.
  47. 47)
      • 29. ‘Matlab Central’. Available at http://www.mathworks.com/matlabcentral/fileexchange/36484-local-binary-patterns/, accessed April 2016.
  48. 48)
      • 40. Salzberg, S.: ‘C4.5: programs for machine learning by J. Ross Quinlan. Morgan Kaufmann Publishers, 1993’, Mach. Learn., 1994, 16, (3), pp. 235–240.