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Rules of photography for image memorability analysis

Rules of photography for image memorability analysis

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Photos are becoming more spread with digital age. Cameras, smart phones and Internet provide large dataset of images available to a wide audience. Assessing memorability of these photos is becoming a challenging task. Besides, finding the best representative model for memorable images will enable memorability prediction. The authors develop a new approach-based rule of photography to evaluate image memorability. In fact, they use three groups of features: image basic features, layout features and image composition features. In addition, they introduce a diversified panel of classifiers based on some data mining techniques used for memorability analysis. They experiment their proposed approach and they compare its results to the state-of-the-art approaches dealing with image memorability. Their approach experiment's results prove that models used in their approach are encouraging predictors for image memorability.

References

    1. 1)
      • P. Isola , J. Xiao , D. Parikh .
        1. Isola, P., Xiao, J., Parikh, D., et al: ‘What makes a photograph memorable?’, IEEE Trans. Pattern Anal. Mach. Intell., 2014, 36, (7), pp. 14691482.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 7 , 1469 - 1482
    2. 2)
      • P. Isola , D. Parikh , A. Torralba .
        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. 24292437.
        . Advances in Neural Information Processing Systems , 2429 - 2437
    3. 3)
      • A. Khosla , J. Xiao , A. Torralba .
        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. 305313.
        . Advances in Neural Information Processing Systems , 305 - 313
    4. 4)
      • M. Mancas , O. Le Meur .
        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. 196200.
        . Proc. IEEE Int. Conf. Image Process. , 196 - 200
    5. 5)
      • C. Redies .
        5. Redies, C.: ‘A universal model of esthetic perception based on the sensory coding of natural stimuli’, Spat. Vis., 2007, 21, (1–2), pp. 97117.
        . Spat. Vis. , 97 - 117
    6. 6)
      • L. Liu , R. Chen , L. Wolf .
        6. Liu, L., Chen, R., Wolf, L., et al: ‘Optimizing photo composition’, Comput. Graph. Forum, 2010, 29, (2), pp. 469478.
        . Comput. Graph. Forum , 2 , 469 - 478
    7. 7)
      • B. Celikkale , A. Erdem , E. Erdem .
        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. 976983.
        . IEEE Computer Vision and Pattern Recognition Workshops , 976 - 983
    8. 8)
      • W. Wang , J. Sun , J. Li .
        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. 573582.
        . Image and Graphics – 8th Int. Conf. , 573 - 582
    9. 9)
      • J. Kim , S. Yoon , V. Pavlovic .
        9. Kim, J., Yoon, S., Pavlovic, V.: ‘Relative spatial features for image memorability’. 21st ACM Int. Conf. Multimedia Proc., Barcelona, Spain, October 2013, pp. 761764.
        . 21st ACM Int. Conf. Multimedia Proc. , 761 - 764
    10. 10)
      • Z. Bylinskii , P. Isola , C. Bainbridge .
        10. Bylinskii, Z., Isola, P., Bainbridge, C., et al: ‘Intrinsic and extrinsic effects on image memorability’, Vis. Res., 2015, 116, pp. 165178.
        . Vis. Res. , 165 - 178
    11. 11)
      • H. Peng , K. Li , B. Li .
        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. 11471150.
        . 23rd ACM Int. Conf. Multimedia Proc. , 1147 - 1150
    12. 12)
      • A. Khosla , A. Raju , A. Torralba .
        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. 23902398.
        . IEEE Int. Conf. Computer Vision , 2390 - 2398
    13. 13)
      • S. Lahrache , R. El Ouazzani , A. El Qadi .
        13. Lahrache, S., El Ouazzani, R., El Qadi, A.: ‘Bag-of-features for image memorability evaluation’, IET Comput. Vis., 2016, 10, (6), pp. 19.
        . IET Comput. Vis. , 6 , 1 - 9
    14. 14)
      • M. Borkin , A.V. Azalea , Z. Bylinskii .
        14. Borkin, M., Azalea, A.V., Bylinskii, Z., et al: ‘What makes a visualization memorable?’, IEEE Trans. Vis. Comput. Graph., 2013, 19, (12), pp. 23062315.
        . IEEE Trans. Vis. Comput. Graph. , 12 , 2306 - 2315
    15. 15)
      • J. Han , C. Chen , L. Shao .
        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. 16921703.
        . IEEE Trans. Cybern. , 8 , 1692 - 1703
    16. 16)
      • T. Aydn , A. Smolic , M. Gross .
        16. Aydn, T., Smolic, A., Gross, M.: ‘Automated aesthetic analysis of photographic images’, IEEE Trans. Vis. Comput. Graph., 2015, 21, (1), pp. 3142.
        . IEEE Trans. Vis. Comput. Graph. , 1 , 31 - 42
    17. 17)
      • K.-Y. Lo , K.-H. Liu , C. Chen .
        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. 533544.
        . Computer Vision – ACCV Workshops , 533 - 544
    18. 18)
      • W.-S. Ng , H.-C. Kao , C.-H. Yeh .
        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.
        .
    19. 19)
      • D. Bora , A. Gupta , F. Khan .
        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. 192203.
        . , 192 - 203
    20. 20)
      • X. Gao , J. Xin , T. Sato .
        20. Gao, X., Xin, J., Sato, T., et al: ‘Analysis of cross-cultural color emotion’, Color Res. Appl., 2007, 32, (3), pp. 223229.
        . Color Res. Appl. , 3 , 223 - 229
    21. 21)
      • J. Schanda . (2007)
        21. Schanda, J.: ‘CIE colorimetry’, in Schanda, J. (Ed.) ‘Colorimetry: understanding the CIE system’ (John Wiley & Sons, Hoboken, NJ, 2007), pp. 2578.
        .
    22. 22)
      • F. Crete , T. Dolmiere , P. Ladret .
        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.
        . Conf. Human Vision and Electronic Imaging XII , 64920I
    23. 23)
      • S. Pertuz , D. Puig , M.A. Garc .
        23. Pertuz, S., Puig, D., Garc, M.A.: ‘Analysis of focus measure operators for shape-from-focus’, Pattern Recognit., 2013, 46, (5), pp. 14151432.
        . Pattern Recognit. , 5 , 1415 - 1432
    24. 24)
      • G. Yang , B. Nelson .
        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. 21432148.
        . Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems , 2143 - 2148
    25. 25)
      • A. Thelen , S. Frey , S. Hirsch .
        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. 151157.
        . IEEE Trans. Image Process. , 1 , 151 - 157
    26. 26)
      • R. Minhas , A. Mohammed , Q. Wu .
        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. 152156.
        . IEEE Trans. Circuits Syst. Video Technol. , 1 , 152 - 156
    27. 27)
      • L. Mai , H. Le , Y. Niu .
        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. 11491152.
        . ACM Multimedia , 1149 - 1152
    28. 28)
      • C. Healey , J. Enns .
        28. Healey, C., Enns, J.: ‘Attention and visual memory in visualization and computer graphics’, IEEE Trans. Vis. Comput. Graph., 2012, 18, (7), pp. 11701188.
        . IEEE Trans. Vis. Comput. Graph. , 7 , 1170 - 1188
    29. 29)
      • 29. ‘Matlab Central’. Available at http://www.mathworks.com/matlabcentral/fileexchange/36484-local-binary-patterns/, accessed April 2016.
        .
    30. 30)
      • G. Loy , O. Eklundh .
        30. Loy, G., Eklundh, O.: ‘Detecting symmetry and symmetric constellations of features’. 9th European Conf. Computer Vision, Graz, Austria, May 2006, pp. 508521.
        . 9th European Conf. Computer Vision , 508 - 521
    31. 31)
      • 31. ‘Digital photography school, how to use leading lines for better composition’. Available at http://digital-photography-school.com/, accessed April 2016.
        .
    32. 32)
      • R. Datta , D. Joshi , J. Li .
        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. 288301.
        . 9th European Conf. Computer Vision , 288 - 301
    33. 33)
      • J. Matas , C. Galambos , J. Kittler .
        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. 119137.
        . Comput. Vis. Image Underst. , 1 , 119 - 137
    34. 34)
      • D. Ballard .
        34. Ballard, D.: ‘Generalizing the Hough transform to detect arbitrary shapes’, Pattern Recognit., 1981, 13, (2), pp. 111122.
        . Pattern Recognit. , 2 , 111 - 122
    35. 35)
      • S. Dhar , V. Ordonez , T. Berg .
        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. 16571664.
        . Computer Vision and Pattern Recognition (CVPR) , 1657 - 1664
    36. 36)
      • L. Mai , H. Le , Y. Niu .
        36. Mai, L., Le, H., Niu, Y., et al: ‘Rule of thirds detection from photograph’. IEEE Int. Symp. Multimedia, CA, USA, December 2011, pp. 9196.
        . IEEE Int. Symp. Multimedia , 91 - 96
    37. 37)
      • U. Fayyad , G.P. Shapiro , P. Smyth .
        37. Fayyad, U., Shapiro, G.P., Smyth, P.: ‘From data mining to knowledge discovery: an overview’, Adv. Knowl. Discov. Data Min., 1996, pp. 134.
        . Adv. Knowl. Discov. Data Min. , 1 - 34
    38. 38)
      • C. Cortes , V. Vapnik .
        38. Cortes, C., Vapnik, V.: ‘Support-vector networks’, Mach. Learn., 1995, 20, (3), pp. 273297.
        . Mach. Learn. , 3 , 273 - 297
    39. 39)
      • M. Orr .
        39. Orr, M.: ‘Introduction to radial basis function networks’. Technical report, Technical Report 4/96, Center for Cognitive Science, University of Edinburgh, 1996.
        .
    40. 40)
      • S. Salzberg .
        40. Salzberg, S.: ‘C4.5: programs for machine learning by J. Ross Quinlan. Morgan Kaufmann Publishers, 1993’, Mach. Learn., 1994, 16, (3), pp. 235240.
        . Mach. Learn. , 3 , 235 - 240
    41. 41)
      • Y. Wang , I.H. Witten .
        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.
        . Proc. Poster Papers of the European Conf. Machine Learning
    42. 42)
      • R. Quinlan .
        42. Quinlan, R.: ‘Learning with continuous classes’. Proc. 5th Australian Joint Conf. Artificial Intelligence, Hobart, Tasmania, November 1992, pp. 343348.
        . Proc. 5th Australian Joint Conf. Artificial Intelligence , 343 - 348
    43. 43)
      • R. Bouckaert , E. Frank , M. Hall .
        43. Bouckaert, R., Frank, E., Hall, M., et al: ‘Weka manual for version 3-7-13’. Technical report, The University of Waikato, 2015.
        .
    44. 44)
      • J. Xiao , J. Hayes , K. Ehinger .
        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. 34853492.
        . Computer Vision and Pattern Recognition (CVPR) , 3485 - 3492
    45. 45)
      • N. Murray , L. Marchesotti , F. Perronnin .
        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. 24082415.
        . Computer Vision and Pattern Recognition (CVPR) , 2408 - 2415
    46. 46)
      • S. Ramanathan , H. Katti , N. Sebe .
        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. 3043.
        . 11th European Conf. Computer Vision , 30 - 43
    47. 47)
      • 47. ‘Amazon Mechanical Turk’. Available at https://www.mturk.com/mturk/welcome/, accessed June 2017.
        .
    48. 48)
      • R. Kohavi .
        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. 11371145.
        . Proc. Fourteenth Int. Joint Conf. Artificial Intelligence (IJCAI) , 1137 - 1145
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