Abstract
In visual password systems it is important to display distractor pictures along with the user’s password picture on the authentication screen. These distractors should be similar to the password picture to make it difficult for shoulder surfers to easily identify the true password picture. In this work, we present approaches to generating similar pictures using bag context picture grammars (BCPGs). We describe how these approaches are implemented and present galleries of pictures that have similar characteristics. We then determine the mathematical similarity of the generated pictures, using the spatial colour distribution descriptor (SpCD). The spatial colour distribution descriptor has been shown to be effective in determining the similarity of computer-generated pictures in previous research, and so was seen as a good similarity measure for this research. We then describe an online survey that was conducted to determine the perceptual similarity of the pictures generated by the picture grammars. This is followed by a comparison of perceptual similarity from the survey with mathematical similarity to determine if the results are consistent. The results show that we can easily generate galleries of pictures that we feel are similar (they have common characteristics) using BCPGs; that SpCD can be used to determine the mathematical similarity of the pictures; and that there is a good correlation between SpCD and perceptual similarity, although in some instances humans do make different judgements. In terms of the desired goal of generating pictures that are similar to a selected picture, we found that controlling the level of refinement of the pictures and limiting the number of refinements of subpictures are important factors.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
The authors used the spelling ‘color’.
- 2.
Please note that we also use the square brackets as shorthand for a set, eg., for \(m \in \mathbb {N}_+\), \(\left[ m\right] = \left\{ 1, 2, \ldots , m\right\} \), as defined earlier. It should be clear from the context which use is meant.
- 3.
Therefore a pictorial form that consists of, say, one square is considered smaller than a pictorial form that consists of, say, three squares, even though both lie in the initial square.
References
Bhika, C., Ewert, S., Schwartz, R., Waruhiu, M.: Table-driven context-free picture grammars. Int. J. Found. Comput. Sci. 18(6), 1151–1160 (2007). https://doi.org/10.1142/S0129054107005194
Chatzichristofis, S.A., Boutalis, Y.S., Lux, M.: Img(Rummager): an interactive content based image retrieval system. In: Proceedings of the Second International Workshop on Similarity Search and Applications, SISAP 2009, pp. 151–153. IEEE Computer Society, Washington, DC (2009). https://doi.org/10.1109/SISAP.2009.16
Chatzichristofis, S.A., Boutalis, Y.S., Lux, M.: SpCD — spatial color distribution descriptor — a fuzzy rule based compact composite descriptor appropriate for hand drawn color sketches retrieval. In: Proceedings of the International Conference on Agents and Artificial Intelligence (ICAART 2010), Valencia, Spain, vol. 1 (Artificial Intelligence), pp. 58–63, January 2010. https://doi.org/10.5220/0002725800580063
Ewert, S.: Random context picture grammars: the state of the art. In: Drewes, F., Habel, A., Hoffmann, B., Plump, D. (eds.) Manipulation of Graphs, Algebras and Pictures, pp. 135–147. Hohnholt, Bremen (2009)
Ewert, S., Jingili, N., Mpota, L., Sanders, I.: Bag context picture grammars. J. Comput. Lang. 51, 214–221 (2019). https://doi.org/10.1016/j.cola.2019.04.001
Goldberger, J., Gordon, S., Greenspan, H.: An efficient image similarity measure based on approximations of KL-divergence between two Gaussian mixtures. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, pp. 487–493. IEEE (2003). https://doi.org/10.1109/ICCV.2003.1238387
Huang, J., Kumar, S., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlograms. In: Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 762–768. IEEE Computer Society (1997). https://doi.org/10.1109/CVPR.1997.609412
Järvelin, K., Kekäläinen, J.: IR evaluation methods for retrieving highly relevant documents. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2000, pp. 41–48. ACM, New York (2000). https://doi.org/10.1145/345508.345545
Jingili, N., Ewert, S., Sanders, I.D.: Measuring perceptual similarity of syntactically generated pictures. In: Rango, F.D., Ören, T.I., Obaidat, M.S. (eds.) Proceedings of 8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2018, Porto, Portugal, 29–31 July 2018. pp. 244–255. SciTePress (2018). https://doi.org/10.5220/0006906502440255
Kiranyaz, S., Birinci, M., Gabbouj, M.: Perceptual color descriptor based on spatial distribution: a top-down approach. Image Vis. Comput. 28(8), 1309–1326 (2010). https://doi.org/10.1016/j.imavis.2010.01.012
Le, Q.V., Smola, A.J.: Direct optimization of ranking measures. CoRR abs/0704.3359 (2007). http://arxiv.org/abs/0704.3359
Li, B., Chang, E., Wu, Y.: Discovery of a perceptual distance function for measuring image similarity. Multimedia Syst. 8(6), 512–522 (2003). https://doi.org/10.1007/s00530-002-0069-9
Neumann, D., Gegenfurtner, K.R.: Image retrieval and perceptual similarity. ACM Trans. Appl. Percept. (TAP) 3(1), 31–47 (2006). https://doi.org/10.1145/1119766.1119769
Okundaye, B., Ewert, S., Sanders, I.: Determining image similarity from pattern matching of abstract syntax trees of tree picture grammars. In: Proceedings of the Twenty-Fourth Annual Symposium of the Pattern Recognition Association of South Africa, pp. 83–90. PRASA, RobMech, AfLaT (2013)
Okundaye, B., Ewert, S., Sanders, I.: Perceptual similarity of images generated using tree grammars. In: Proceedings of the Annual Conference of the South African Institute for Computer Scientists and Information Technologists (SAICSIT 2014), pp. 286–296. ACM (2014). https://doi.org/10.1145/2664591.2664606
Swain, M.J., Ballard, D.H.: Color indexing. Int. J. Comput. Vis. 7(1), 11–32 (1991). https://doi.org/10.1007/BF00130487
Yamamoto, H., Iwasa, H., Yokoya, N., Takemura, H.: Content-based similarity retrieval of images based on spatial color distributions. In: Proceedings of the 10th International Conference on Image Analysis and Processing, pp. 951–956. IEEE (1999). https://doi.org/10.1109/ICIAP.1999.797718
Zhou, X.S., Huang, T.S.: Relevance feedback in image retrieval: a comprehensive review. Multimedia Syst. 8(6), 536–544 (2003). https://doi.org/10.1007/s00530-002-0070-3
Acknowledgements
This work is based upon research supported by the National Research Foundation (of South Africa). Any opinion, findings and conclusions or recommendations expressed in this material are those of the authors and therefore the NRF does not accept liability in regard thereto. The first author also acknowledges support of the Council for Scientific and Industrial Research of South Africa.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Jingili, N., Ewert, S., Sanders, I. (2020). Syntactic Generation of Similar Pictures. In: Obaidat, M., Ören, T., Rango, F. (eds) Simulation and Modeling Methodologies, Technologies and Applications. SIMULTECH 2018. Advances in Intelligent Systems and Computing, vol 947. Springer, Cham. https://doi.org/10.1007/978-3-030-35944-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-35944-7_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35943-0
Online ISBN: 978-3-030-35944-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)