Skip to main content
SpringerLink
Log in

Minimal neural network topology optimization for aesthetic classification

  • S. I : Neural Networks in Art, sound and Design
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In this article, a minimum neural network topology in terms of units and connections (neurons and weights), making visual aesthetically categorized images, will be identified. For this purpose, an analysis to compact the initial neural network will be conducted by reducing the amount of connections and neurons required for an a esthetic classification function in hidden layers. A collection of photographs and aesthetic evaluations from an online voting site for photographs such as Photo.net was used. Such pictures are defined by ad-hoc metrics used in previous studies. In this way, the input data, the images, are interpreted within a minimum complexity scheme by complexity estimators (internal representations). The results obtained show that the optimized topology found retains both its performance and its ability to generalize. As part of this study, it has been found a relationship concerning the input data and minimum topology necessary for its correct representation, demonstrating statistically a performance comparable to other not minimized topologies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Ambroise C, McLachlan GJ (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Proc Natl Acad Sci 99(10):6562–6566. https://doi.org/10.1073/pnas.102102699

    Article  MATH  Google Scholar 

  2. Arnheim R (1954) Art and visual perception: a psychology of the creative eye. University of California Press, Oakland

    Google Scholar 

  3. Birkhoff GD (1933) Aesthetic measure. Mass, Cambridge

    MATH  Google Scholar 

  4. Blum A (1992) Neural networks in C++: an object-oriented framework for building connectionist systems. John Wiley and Sons Inc, New York

    Google Scholar 

  5. Bu Y, Zhao G, Luo AL, Pan J, Chen Y (2015) Restricted boltzmann machine: a non-linear substitute for pca in spectral processing. Astron Astrophys 576:A96. https://doi.org/10.1051/0004-6361/201424194

    Article  Google Scholar 

  6. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698

    Article  Google Scholar 

  7. Carballal A, Castro L, Perez R, Correia J (2014) Detecting bias on aesthetic image datasets. Int J Creat Interfaces Comput Gr (IJCICG) 5(2):62–74

    Article  Google Scholar 

  8. Carballal A, Fernandez-Lozano C, Heras J, Romero J (2019) Transfer learning features for predicting aesthetics through a novel hybrid machine learning method. Neural Comput Appl 32:1–12

    Google Scholar 

  9. Carballal A, Fernandez-Lozano C, Rodriguez-Fernandez N, Castro L, Santos A (2019) Avoiding the inherent limitations in datasets used for measuring aesthetics when using a machine learning approach. Complexity

  10. Chan MC, Wong CC, Lam CC (2000) Financial time series forecasting by neural network using conjugate gradient learning algorithm and multiple linear regression weight initialization. Comput Econ Finance 61:326–342

    Google Scholar 

  11. Chang CC, Lin CJ (2011) Libsvm: A library for support vector machines. ACM Trans Intell Syst Technol 2(3) https://doi.org/10.1145/1961189.1961199

  12. Datta R, Joshi D, Li J, Wang JZ (2006) Studying aesthetics in photographic images using a computational approach. computer vision-ECCV 2006. Springer, Berlin, pp 288–301

    Google Scholar 

  13. Datta R, Jia Li, Wang JZ (2008) Algorithmic inferencing of aesthetics and emotion in natural images: an exposition. In: 2008 15th IEEE international conference on image processing, pp 105–108

  14. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: CVPR09

  15. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1(1):3–18. https://doi.org/10.1016/j.swevo.2011.02.002

    Article  Google Scholar 

  16. Dong Z, Tian X (2015) Multi-level photo quality assessment with multi-view features. Neurocomputing 168:308–319

    Article  Google Scholar 

  17. Eysenck HJ (1941) The empirical determination of an aesthetic formula. Psychol Rev 48(1):83

    Article  Google Scholar 

  18. Fan Y, Xu K, Wu H, Zheng Y, Tao B (2020) Spatiotemporal modeling for nonlinear distributed thermal processes based on kl decomposition, mlp and lstm network. IEEE Access 8:25111–25121

    Article  Google Scholar 

  19. Fernandez-Lozano C, Gestal M, Munteanu CR, Dorado J, Pazos A (2016) A methodology for the design of experiments in computational intelligence with multiple regression models. PeerJ 4:e2721

    Article  Google Scholar 

  20. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422. https://doi.org/10.1023/A:1012487302797

    Article  MATH  Google Scholar 

  21. Holland JH et al (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, Cambridge

    Book  Google Scholar 

  22. Jia G, Li P, He R (2019) Theme aware aesthetic distribution prediction with full resolution photos. CoRR abs/1908.01308, http://arxiv.org/abs/1908.01308, 1908.01308

  23. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks, IEEE, vol 4, pp 1942–1948

  24. Kong S, Shen X, Lin Z, Mech R, Fowlkes CC (2016) Photo aesthetics ranking network with attributes and content adaptation. CoRR abs/1606.01621, http://arxiv.org/abs/1606.01621, 1606.01621

  25. Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  26. Lilliefors HW (1967) On the kolmogorov–smirnov test for normality with mean and variance unknown. J Am Stat Assoc 62(318):399–402

    Article  Google Scholar 

  27. Liu SC, Chang S (1997) Dimension estimation of discrete-time fractional Brownian motion with applications to image texture classification. IEEE Trans Image Process 6(8):1176–1184

    Article  Google Scholar 

  28. Machado P, Cardoso A (1998) Computing aesthetics. Advances in artificial intelligence. Springer, Berlin, pp 219–228

    Book  Google Scholar 

  29. Machado P, Romero J, Santos ML, Cardoso A, Manaris B (2004) Adaptive critics for evolutionary artists. Applications of evolutionary computing. Springer, Berlin, pp 437–446

    Google Scholar 

  30. Machado P, Romero J, Manaris B (2008) Experiments in computational aesthetics. The art of artificial evolution. Springer, Berlin, pp 381–415

    Book  Google Scholar 

  31. Machado P, Romero J, Nadal M, Santos A, Correia J, Carballal A (2015) Computerized measures of visual complexity. Acta Psychol 160:43–57. https://doi.org/10.1016/j.actpsy.2015.06.005

    Article  Google Scholar 

  32. McClelland JL, Rumelhart DE, Group PR et al (1986) Parallel distributed processing. Explor Microstruct Cognit 2:216–271

    Google Scholar 

  33. McIntosh A (2016) The jackknife estimation method. arXiv preprint arXiv:160600497

  34. McKnight PE, Najab J (2010) Mann–whitney \(u\) test. In: Corsini encyclopedia of psychology. John Wiley & Sons Inc, New York, USA, vol 1, p 1

  35. Meier NC (1942) Art in human affairs; an introduction to the psychology of art. McGraw-Hill, New York

    Google Scholar 

  36. Melit Devassy B, George S (2020) Dimensionality reduction and visualisation of hyperspectral ink data using t-sne. Forensic Sci Int 311(110):194. https://doi.org/10.1016/j.forsciint.2020.110194

    Article  Google Scholar 

  37. Moles AA (1957) Théorie de l’information et perception esthétique. Revue Philosophique de la France et de l’Étranger 147:233–242

    Google Scholar 

  38. Murray N, Marchesotti L, Perronnin F (2012) Ava: A large-scale database for aesthetic visual analysis. In: 2012 IEEE conference on computer vision and pattern recognition, IEEE, pp 2408–2415

  39. Peres-Neto PR, Jackson DA, Somers KM (2005) How many principal components? Stopping rules for determining the number of non-trivial axes revisited. Comput Stat Data Anal 49:974–997

    Article  MathSciNet  Google Scholar 

  40. Probst P, Bischl B, Boulesteix AL (2018) Tunability: importance of hyperparameters of machine learning algorithms. 1802.09596

  41. Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386–408

    Article  Google Scholar 

  42. Ramezan CA, Warner TA, Maxwell AE (2019) Evaluation of sampling and cross-validation tuning strategies for regional-scale machine learning classification. Remote Sens 11(2):185

    Article  Google Scholar 

  43. Russell S, Norvig P (2009) Artificial intelligence: a modern approach, 3rd edn. Prentice Hall Press, New Jersey

    MATH  Google Scholar 

  44. Saunders R, Gero JS (2001) Artificial creativity: a synthetic approach to the study of creative behaviour. Computational and Cognitive Models of Creative Design V, Key Centre of Design Computing and Cognition, University of Sydney, Sydney pp 113–139

  45. Shamshirband S, Rabczuk T, Chau K (2019) A survey of deep learning techniques: application in wind and solar energy resources. IEEE Access 7:164650–164666

    Article  Google Scholar 

  46. Sobel I (1990) An isotropic 3x3 image gradient operator. In: Freeman H (ed) Machine vision for three-dimensional scenes. Academic Press, London, pp 376–379

    Google Scholar 

  47. Svangård N, Nordin P (2004) Automated aesthetic selection of evolutionary art by distance based classification of genomes and phenomes using the universal similarity metric. Applications of evolutionary computing. Springer, Berlin, pp 447–456

    Google Scholar 

  48. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2015) Rethinking the inception architecture for computer vision. CoRR abs/1512.00567, http://arxiv.org/abs/1512.00567, 1512.00567

  49. Taylor RP, Micolich AP, Jonas D (1999) Fractal analysis of pollock’s drip paintings. Nature 399(6735):422

    Article  Google Scholar 

  50. Wang WC, Xu L, Chau KW, Xu DM (2020) Yin-yang firefly algorithm based on dimensionally cauchy mutation. Expert Syst Appl 150(113):216. https://doi.org/10.1016/j.eswa.2020.113216

    Article  Google Scholar 

  51. Williams RJ, Zipser D (1989) A learning algorithm for continually running fully recurrent neural networks. Neural Comput 1(2):270–280

    Article  Google Scholar 

  52. Yan K, Xiaoou T, Feng J (2006) The design of high-level features for photo quality assessment. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06), vol 1, pp 419–426

  53. Zipf GK (1949) Human behavior and the principle of least effort. Addison-Wesley Press, Boston

    Google Scholar 

Download references

Acknowledgements

This work is supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431D 201716) and Competitive Reference Groups (Ref. ED431C 201849). This work has also been supported by CITIC, as a Research Centre of the Galician University System, is financed by the Regional Ministry of Education, University and Vocational Training of the Xunta de Galicia through the European Regional Development Fund (ERDF) with 80%, Operational Programme ERDF Galicia 2014-2020, and the remaining 20% by the General Secretariat of Universities (Ref. ED431G 2019/01). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, 15071, A Coruña, Spain

    Adrian Carballal, Francisco Cedron, Antonino Santos & Juan Romero

  2. CITIC - Research Center of Information and Communication Technologies, University of A Coruña, 15071, A Coruña, Spain

    Adrian Carballal, Francisco Cedron, Iria Santos, Antonino Santos & Juan Romero

Authors
  1. Adrian Carballal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francisco Cedron
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Iria Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Antonino Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Juan Romero
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adrian Carballal.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Carballal, A., Cedron, F., Santos, I. et al. Minimal neural network topology optimization for aesthetic classification. Neural Comput & Applic 33, 107–119 (2021). https://doi.org/10.1007/s00521-020-05550-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05550-x

Keywords

Search

Navigation