Abstract
In this work we present a method for selecting instances for a writer identification system underpinned on the dissimilarity representation and a holistic representation based on texture. The proposed method is based on a genetic algorithm that surpasses the limitations imposed by large training sets by selecting writers instead of instances. To show the efficiency of the proposed method, we have performed experiments on three different databases (BFL, IAM, and Firemaker) where we can observe not only a reduction of about 50% in the number of writers necessary to build the dissimilarity model but also a gain in terms of identification rate. Comparing the writer selection with the traditional instance selection, we could observe that both strategies produce similar results but the former converges about three times faster.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Bertolini, D., Oliveira, L.S., Justino, E., Sabourin, R.: Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers. Pattern Recognition 43(1), 387–396 (2010)
Bertolini, D., Oliveira, L.S., Justino, E., Sabourin, R.: Texture-based descriptors for writer identification and verification. Expert Systems With Applications 40(6), 2069–2080 (2013)
Bezdek, J.C., Kuncheva, L.: Nearest prototype classifier designs: an experimental study. International Journal of Hybrid Intelligent Systems 16(12), 1445–14473 (2001)
Freitas, C., Oliveira, L.S., Sabourin, R., Bortolozzi, F.: Brazilian forensic letter database. In: 11th Int. Workshop on Frontiers on Handwriting Recognition (2008)
Garcia, S., Derrac, J., Cano, J.R., Herrera, F.: Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Trans. on Pattern Anaysis and Machine Intelligence 34(3), 417–435 (2012)
Lopes, J., Ochoa, J., Trinidad, J., Kittler, J.: A review of instance selection methods. Artificial Intelligence Review 34, 133–143 (2010)
Lopes, J., Trinidad, J., Ochoa, J., Kittler, J.: Prototype selection based on sequeintial search. Intelligent Data Analysis 13(4), 599–631 (2009)
Marti, U.V., Bunke, H.: The IAM-database: an english sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition 5(1), 39–46 (2002)
Pekalska, E., Duin, R.P.W.: Dissimilarity representations allow for building good classifiers. Pattern Recognition 23, 943–956 (2002)
Schomaker, L., Vuurpijl, L.: Forensic writer identification: A benchmark data set and a comparison of two systems. Technical report, Nijmegen, February 2000
Zhang, H., Sun, G.: Optimal reference subset selection for nearest neighbor classification by tabu search. Pattern Recognition 35, 1481–1490 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Bertolini, D., Oliveira, L.S., Sabourin, R. (2015). Improving Writer Identification Through Writer Selection. In: Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science(), vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-25751-8_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25750-1
Online ISBN: 978-3-319-25751-8
eBook Packages: Computer ScienceComputer Science (R0)