skip to main content
10.1145/3347317.3357244acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
research-article

Organizing Cultural Heritage with Deep Features

Published: 15 October 2019 Publication History

Abstract

In recent years, the preservation and diffusion of culture in the digital form has been a priority for the governments in different countries, as in Mexico, with the objective of preserving and spreading culture through information technologies. Nowadays, a large amount of multimedia content is produced. Therefore, more efficient and accurate systems are required to organize it. In this work, we analyze the ability of a pre-trained residual network (ResNet) to describe information through the extracted deep features and we analyze its behavior by grouping new data into clusters by the K-means method at different levels of compression with the PCA algorithm showing that the structuring of new input data can be done with the proposed method.

References

[1]
Artem Babenko, Anton Slesarev, Alexandr Chigorin, and Victor Lempitsky. 2014. Neural codes for image retrieval. In European conference on computer vision. Springer, 584--599.
[2]
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. 2015. Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274 (2015).
[3]
Kamel Guissous and Valérie Gouet-Brunet. 2017. Image retrieval based on saliency for urban image contents. In 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 1--6.
[4]
Jonathan Harel, Christof Koch, and Pietro Perona. 2007. Graph-based visual saliency. In Advances in neural information processing systems. 545--552.
[5]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.
[6]
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, and Ngai-Man Cheung. 2017. Selective deep convolutional features for image retrieval. In Proceedings of the 25th ACM international conference on Multimedia. ACM, 1600--1608.
[7]
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.
[8]
Abraham Montoya Obeso, Jenny Benois-Pineau, Mireya Sara'i Garc'ia Vázquez, and Alejandro Alvaro Ram'irez Acosta. 2018a. Introduction of Explicit Visual Saliency in Training of Deep CNNs: Application to Architectural Styles Classification. 2018 International Conference on Content-Based Multimedia Indexing (CBMI) (2018), 1--5.
[9]
Abraham Montoya Obeso, Jenny Benois-Pineau, Mireya Saraí Garc'ia Vázquez, Alejandro Álvaro Rmírez Acosta, Kamel Guissous, and Valérie Gouet-Brunet. 2018b. Comparative study of visual saliency maps in the problem of classification of architectural images with Deep CNNs. 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) (2018), 1--6.
[10]
Abraham Montoya Obeso, Laura Mariel Amaya Reyes, Mario Lopez Rodriguez, Mario Humberto Mijes Cruz, Mireya Sara'i Garc'ia Vázquez, Jenny Benois-Pineau, Luis Miguel Zamudio Fuentes, Elizabeth Cano Martinez, Jesús Abimelek Flores Secundino, José Luis Rivera Martinez, et almbox. 2016. Image annotation for Mexican buildings database. In Optics and Photonics for Information Processing X, Vol. 9970. International Society for Optics and Photonics, 99700Y.
[11]
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, Vol. 12 (2011), 2825--2830.
[12]
Andrew Rosenberg and Julia Hirschberg. 2007. V-measure: A conditional entropy-based external cluster evaluation measure. In Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL) . 410--420.
[13]
Peter J Rousseeuw. 1987. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, Vol. 20 (1987), 53--65.
[14]
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1--9.
[15]
UNESCO. 2003. Mexico - intangible heritage - Culture Sector - UNESCO . https://ich.unesco.org/en/state/mexico-MX

Cited By

View all
  • (2020)A Neural Networks Approach to Detecting Lost Heritage in Historical VideoISPRS International Journal of Geo-Information10.3390/ijgi90502979:5(297)Online publication date: 5-May-2020

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SUMAC '19: Proceedings of the 1st Workshop on Structuring and Understanding of Multimedia heritAge Contents
October 2019
87 pages
ISBN:9781450369107
DOI:10.1145/3347317
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2019

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. data structuring
  2. neural networks
  3. visual attention

Qualifiers

  • Research-article

Funding Sources

  • Eiffel Excellence Scholarship Program
  • SIP2019
  • CONACYT Student Grant

Conference

MM '19
Sponsor:

Acceptance Rates

Overall Acceptance Rate 5 of 6 submissions, 83%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)3
  • Downloads (Last 6 weeks)0
Reflects downloads up to 10 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2020)A Neural Networks Approach to Detecting Lost Heritage in Historical VideoISPRS International Journal of Geo-Information10.3390/ijgi90502979:5(297)Online publication date: 5-May-2020

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media