research-article

Improved Hieroglyph Representation for Image Retrieval

Authors:

Laura Alejandra Pinilla-Buitrago,

Jesús Ariel Carrasco-Ochoa,

José Fco Martínez-Trinidad,

Edgar Román-RangelAuthors Info & Claims

Journal on Computing and Cultural Heritage (JOCCH), Volume 12, Issue 2

Article No.: 13, Pages 1 - 15

https://doi.org/10.1145/3284388

Published: 30 April 2019 Publication History

Abstract

In recent years, an interdisciplinary effort between archaeologists and computer vision experts has emerged to provide image retrieval tools that facilitate and support cultural heritage preservation. The performance of these tools largely depends on the hieroglyph representation quality. In the literature, the most successful hieroglyph representation for retrieval following the BoVW model includes a thinning hieroglyph process and selects interest points through uniform random sampling. However, thinned hieroglyphs could have noise or redundant information, and a random set of interest points could include non-useful interest points that are different in each iteration. In this article, we propose improving this hieroglyph representation by pruning thinned hieroglyphs and introducing an improved interest-point selection. Our experiments show that our proposal significantly improves the hieroglyph retrieval results of state-of-the-art methods.

References

[1]

X. Bai, L. J. Latecki, and W.-y. Liu. 2007. Skeleton pruning by contour partitioning with discrete curve evolution. IEEE Trans. Pattern Anal. Mach. Intell. 29, 3 (March 2007), 449--462.

Digital Library

[2]

S. Belongie, J. Malik, and J. Puzicha. 2002. Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24, 4 (Apr. 2002), 509--522.

Digital Library

[3]

Gülcan Can, Jean-Marc Odobez, and Daniel Gatica-Perez. 2017. Shape representations for Maya codical glyphs: Knowledge-driven or deep? In Proceedings of the 15th International Workshop on Content-based Multimedia Indexing (CBMI’17). ACM, Article 32, 6 pages.

Digital Library

[4]

Gülcan Can, Jean-Marc Odobez, and Daniel Gatica-Perez. 2018. How to tell ancient signs apart? Recognizing and visualizing Maya glyphs with CNNs. ACM J. Comput. Cult. Her. (May 2018).

Digital Library

[5]

Vincenzo Deufemia and Luca Paolino. 2013. Combining unsupervised clustering with a non-linear deformation model for efficient petroglyph recognition. In Advances in Visual Computing, George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Baoxin Li, Fatih Porikli, Victor Zordan, James Klosowski, Sabine Coquillart, Xun Luo, Min Chen, and David Gotz (Eds.). Springer Berlin, 128--137.

[6]

Vincenzo Deufemia and Luca Paolino. 2014. Segmentation and recognition of petroglyphs using generic Fourier descriptors. In Image and Signal Processing, Abderrahim Elmoataz, Olivier Lezoray, Fathallah Nouboud, and Driss Mammass (Eds.). Springer International Publishing, Cham, 487--494.

[7]

V. Deufemia, L. Paolino, and H. de Lumley. 2012. Petroglyph recognition using self-organizing maps and fuzzy visual language parsing. In Proceedings of the IEEE 24th International Conference on Tools with Artificial Intelligence, Vol. 1. 852--859.

Digital Library

[8]

David H. Douglas and Thomas K. Peucker. 1973. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: Int. J. Geogr. Inform. Geovis. 10, 2 (1973), 112--122.

[9]

Jaime Duque-Domingo, Pedro Javier Herrera, Enrique Valero, and Carlos Cerrada. 2017. Deciphering Egyptian hieroglyphs: Towards a new strategy for navigation in museums. Sensors 17, 3 (2017).

[10]

Rafael C. Gonzalez and Richard E. Woods. 2006. Digital Image Processing (3rd ed.). Prentice-Hall, Inc., Upper Saddle River, NJ. 653--655.

Digital Library

[11]

Rui Hu, Carlos Pallan Gayol, Jean-Marc Odobez, and Daniel Gatica-Perez. 2017. Analyzing and visualizing ancient Maya hieroglyphics using shape: From computer vision to digital humanities. Dig. Schol. Hum. 32 (2017), 179--194.

[12]

J. K. Lakshmi and M. Punithavalli. 2009. A survey on skeletons in digital image processing. In Proceedings of the International Conference on Digital Image Processing. 260--269.

Digital Library

[13]

Longin Jan Latecki and Rolf Lakämper. 1999. Convexity rule for shape decomposition based on discrete contour evolution. Comput. Vis. Image Und. 73 (1999), 441--454.

Digital Library

[14]

Longin Jan Latecki and Rolf Lakämper. 1999. Polygon evolution by vertex deletion. In Proceedings of the 2nd International Conference on Scale-Space Theories in Computer Vision (SCALE-SPACE’99). Springer-Verlag, 398--409.

Digital Library

[15]

Hongzhi Liu, Zhonghai Wu, D. Frank Hsu, Bradley S. Peterson, and Dongrong Xu. 2012. On the generation and pruning of skeletons using generalized Voronoi diagrams. Pattern Recog. Lett. 33, 16 (2012), 2113-2119.

Digital Library

[16]

HongZhi Liu, Zhong-Hai Wu, Xing Zhang, and D. Frank Hsu. 2013. A skeleton pruning algorithm based on information fusion. Pattern Recog. Lett. 34, 10 (2013), 1138--1145.

Digital Library

[17]

David G. Lowe. 2004. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 2 (2004), 91--110.

Digital Library

[18]

Krystian Mikolajczyk and Cordelia Schmid. 2004. Scale and affine invariant interest point detectors. Int. J. Comput. Vis. 60, 1 (2004), 63--86.

Digital Library

[19]

Greg Mori, Serge Belongie, and Jitendra Malik. 2005. Efficient shape matching using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 27, 11 (Nov. 2005), 1832--1837.

Digital Library

[20]

Thanh Phuong Nguyen and Isabelle Debled-Rennesson. 2011. A discrete geometry approach for dominant point detection. Pattern Recog. 44, 1 (2011), 32--44.

Digital Library

[21]

Laura Alejandra Pinilla-Buitrago, José Fco. Martínez-Trinidad, and J. A. Carrasco-Ochoa. 2014. A new method for skeleton pruning. In Pattern Recog., José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-Lopez, Joaquín Salas-Rodríguez, and Ching Y. Suen (Eds.). Lecture Notes in Computer Science, Vol. 8495. Springer International Publishing, 301--310.

[22]

D. K. Prasad, Chai Quek, M. K. H. Leung, and Siu-Yeung Cho. 2011. A parameter independent line fitting method. In Proceedings of the 1st Asian Conference on Pattern Recognition (ACPR’11). 441--445.

[23]

Edgar Roman-Rangel and Stephane Marchand-Maillet. 2014. HOOSC128: A more robust local shape descriptor. In Pattern Recog., José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-Lopez, Joaquín Salas-Rodríguez, and Ching Y. Suen (Eds.). Lecture Notes in Computer Science, Vol. 8495. Springer International Publishing, 172--181.

[24]

Edgar Roman-Rangel and Stephane Marchand-Maillet. 2015. Shape-based detection of Maya hieroglyphs using weighted bag representations. Pattern Recog. 48, 4 (2015), 1161--1173.

Digital Library

[25]

Edgar Roman-Rangel, Carlos Pallan Gayol, Jean-Marc Odobez, and Daniel Gatica-Perez. 2011. Analyzing ancient Maya glyph collections with contextual shape descriptors. Int. J. Comput. Vis. 94, 1 (2011), 101--117.

Digital Library

[26]

Edgar Roman-Rangel, Carlos Pallan Gayol, Jean-Marc Odobez, and Daniel Gatica-Perez. 2011. Searching the past: An improved shape descriptor to retrieve Maya hieroglyphs. In Proceedings of the 19th ACM International Conference on Multimedia (MM’11). ACM, 163--172.

Digital Library

[27]

Edgar Roman-Rangel, Carlos Pallan Gayol, Jean-Marc Odobez, and Daniel Gatica-Perez. 2013. Evaluating shape descriptors for detection of Maya hieroglyphs. In Pattern Recog., Jesús Ariel Carrasco-Ochoa, José Francisco Martínez-Trinidad, Joaquín Salas Rodríguez, and Gabriella Sanniti di Baja (Eds.). Lecture Notes in Computer Science, Vol. 7914. Springer Berlin, 145--154.

[28]

Punam K. Saha, Gunilla Borgefors, and Gabriella Sanniti di Baja. 2016. A survey on skeletonization algorithms and their applications. Pattern Recog. Lett. 76 (2016), 3--12. Special Issue on Skeletonization and Its Application.

Digital Library

[29]

Markus Seidl. 2016. Computation Analysis of Petroglyphs. Ph.D. Dissertation. Technische Universitat Wien, Vienna, Austria.

[30]

Markus Seidl and Christian Breiteneder. 2011. Detection and classification of petroglyphs in gigapixel images. In Proceedings of the International Symposium on Virtual Reality, Archaeology and Intelligent Cultural Heritage (VAST’11)—Short and Project Papers, Franco Niccolucci, Matteo Dellepiane, Sebastian Pena Serna, Holly Rushmeier, and Luc Van Gool (Eds.). The Eurographics Association.

[31]

Markus Seidl, Ewald Wieser, and Craig Alexander. 2015. Automated classification of petroglyphs. Dig. Appl. Arch. Cult. Her. 2, 2 (2015), 196--212.

[32]

Markus Seidl, Ewald Wieser, Matthias Zeppelzauer, Axel Pinz, and Christian Breiteneder. 2015. Graph-based shape similarity of petroglyphs. In Computer Vision—ECCV 2014 Workshops, Lourdes Agapito, Michael M. Bronstein, and Carsten Rother (Eds.). Springer International Publishing, Cham, 133--148.

[33]

Luca Serino and Gabriella Sanniti di Baja. 2016. A new strategy for skeleton pruning. Pattern Recog. Lett. 76, Supplement C (2016), 41--48. Special Issue on Skeletonization and Its Application.

Digital Library

[34]

Wei Shen, Xiang Bai, Rong Hu, Hongyuan Wang, and Longin Jan Latecki. 2011. Skeleton growing and pruning with bending potential ratio. Pattern Recog. 44, 2 (2011), 196--209.

Digital Library

[35]

Wei Shen, Xiang Bai, Xingwei Yang, and Longin Jan Latecki. 2013. Skeleton pruning as trade-off between skeleton simplicity and reconstruction error. Sci. China Inform. Sci. 56, 4 (Apr. 2013), 1--14.

[36]

J. Eric S. Thompson. 1962. A Catalog of Maya Hieroglyphs. University of Oklahoma Press.

[37]

Xinggang Wang, Bin Feng, Xiang Bai, Wenyu Liu, and Longin Jan Latecki. 2014. Bag of contour fragments for robust shape classification. Pattern Recog. 47, 6 (2014), 2116--2125.

Digital Library

[38]

Ewald Wieser, Markus Seidl, and Matthias Zeppelzauer. 2017. A study on skeletonization of complex petroglyph shapes. Mult. Tools Appl. 76, 6 (Mar 2017), 8285--8303.

Digital Library

[39]

T. Y. Zhang and C. Y. Suen. 1984. A fast parallel algorithm for thinning digital patterns. Commun. ACM 27, 3 (March 1984), 236--239.

Digital Library

[40]

Qiang Zhu, Xiaoyue Wang, Eamonn Keogh, and Sang-Hee Lee. 2011. An efficient and effective similarity measure to enable data mining of petroglyphs. Data Mining Knowl. Discov. 23, 1 (Jul 2011), 91--127.

Digital Library

Cited By

Melnik GYekutieli YSharf A(2022)Deep Segmentation of Corrupted GlyphsJournal on Computing and Cultural Heritage 10.1145/346562915:1(1-24)Online publication date: 22-Jan-2022
https://dl.acm.org/doi/10.1145/3465629
Pinilla-Buitrago LMartínez-Trinidad JCarrasco-Ochoa J(2022)Encoding hieroglyph segments to represent hieroglyphs following the bag of visual word model for retrievalExpert Systems with Applications10.1016/j.eswa.2022.116983201(116983)Online publication date: Sep-2022
https://doi.org/10.1016/j.eswa.2022.116983

Index Terms

Improved Hieroglyph Representation for Image Retrieval
1. Applied computing
  1. Arts and humanities
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation
      1. Image processing
    2. Shape modeling
      1. Shape analysis

Recommendations

Automatic Egyptian hieroglyph recognition by retrieving images as texts
MM '13: Proceedings of the 21st ACM international conference on Multimedia

In this paper we propose an approach for automatically recognizing ancient Egyptian hieroglyph from photographs. To this end we first manually annotated and segmented a large collection of nearly 4,000 hieroglyphs. In our automatic approach we localize ...
Encoding hieroglyph segments to represent hieroglyphs following the bag of visual word model for retrieval
Abstract
The representation of hieroglyphs in retrieval systems represents a great challenge since these systems’ results highly depend on the used representation. In the literature, the most successful works in this area compute local ...
Highlights
- Hieroglyph representation for retrieval based on encoding hieroglyph segments.
- ...
Cultural Relic Image Retrieval Method Based on Features of SIFT
ICCIS '11: Proceedings of the 2011 International Conference on Computational and Information Sciences

Aiming at the cultural relic image retrieval problems in digital archaeology museum of Northwest University, an image retrieval method based on Scale Invariant Feature Transform (SIFT) features is proposed in this paper. Firstly, SIFT features of ...

Comments

Information & Contributors

Information

Published In

cover image Journal on Computing and Cultural Heritage

Journal on Computing and Cultural Heritage Volume 12, Issue 2

June 2019

153 pages

ISSN:1556-4673

EISSN:1556-4711

DOI:10.1145/3328727

Editor:
Franco Niccolucci
VAST-LAB at PIN, University of Florence, Italy

Issue’s Table of Contents

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 April 2019

Accepted: 01 October 2018

Revised: 01 October 2018

Received: 01 February 2018

Published in JOCCH Volume 12, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

National Council of Science and Technology of México (CONACyT)

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
192
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Melnik GYekutieli YSharf A(2022)Deep Segmentation of Corrupted GlyphsJournal on Computing and Cultural Heritage 10.1145/346562915:1(1-24)Online publication date: 22-Jan-2022
https://dl.acm.org/doi/10.1145/3465629
Pinilla-Buitrago LMartínez-Trinidad JCarrasco-Ochoa J(2022)Encoding hieroglyph segments to represent hieroglyphs following the bag of visual word model for retrievalExpert Systems with Applications10.1016/j.eswa.2022.116983201(116983)Online publication date: Sep-2022
https://doi.org/10.1016/j.eswa.2022.116983

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents