Method for the recovery of indexed images in databases from visual content
The techniques of content-based image recovery (CBIR) provide a solution to a problem of information retrieval that may arise as follows: from an image of interest to recover or obtain similar images from among those present in a large collection, using only features or features extracted from said...
- Autores:
-
amelec, viloria
Varela Izquierdo, Noel
Vargas, Jesús
Pineda, Omar
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7276
- Acceso en línea:
- https://hdl.handle.net/11323/7276
https://repositorio.cuc.edu.co/
- Palabra clave:
- Convolutional neural networks
Global descriptors
Image retrieval
Information retrieval
- Rights
- closedAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Method for the recovery of indexed images in databases from visual content |
| title |
Method for the recovery of indexed images in databases from visual content |
| spellingShingle |
Method for the recovery of indexed images in databases from visual content Convolutional neural networks Global descriptors Image retrieval Information retrieval |
| title_short |
Method for the recovery of indexed images in databases from visual content |
| title_full |
Method for the recovery of indexed images in databases from visual content |
| title_fullStr |
Method for the recovery of indexed images in databases from visual content |
| title_full_unstemmed |
Method for the recovery of indexed images in databases from visual content |
| title_sort |
Method for the recovery of indexed images in databases from visual content |
| dc.creator.fl_str_mv |
amelec, viloria Varela Izquierdo, Noel Vargas, Jesús Pineda, Omar |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria Varela Izquierdo, Noel Vargas, Jesús Pineda, Omar |
| dc.subject.spa.fl_str_mv |
Convolutional neural networks Global descriptors Image retrieval Information retrieval |
| topic |
Convolutional neural networks Global descriptors Image retrieval Information retrieval |
| description |
The techniques of content-based image recovery (CBIR) provide a solution to a problem of information retrieval that may arise as follows: from an image of interest to recover or obtain similar images from among those present in a large collection, using only features or features extracted from said images Banuchitra and Kungumaraj (Int J Eng Comput Sci (IJECS) 5 (2016) [1]). Similar images are understood as those in which the same object or scene is observed with variations in perspective, lighting conditions or scale. The stored images are preprocessed and then their corresponding descriptors are indexed. The query image is also preprocessed to extract its descriptor, which is then compared to those stored by applying appropriate similarity measures, which allow the recovery of those images that are similar to the query image. In the present work, a method was developed for the recovery of indexed images in databases from their visual content, without the need to make textual annotations. Feature vectors were obtained from visual contents using artificial neural network techniques with deep learning. |
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2020 |
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2020-11-12T17:33:39Z |
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2020-11-12T17:33:39Z |
| dc.date.issued.none.fl_str_mv |
2020 |
| dc.date.embargoEnd.none.fl_str_mv |
2021-01-31 |
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Pre-Publicación |
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2194-5357 |
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https://hdl.handle.net/11323/7276 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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eng |
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Banuchitra S, Kungumaraj K (2016) A comprehensive survey of content based image retrieval techniques. Int J Eng Comput Sci (IJECS) 5 (2016). https://ezproxy.cuc.edu.co:2067/10.18535/ijecs/v5i8.26. https://www.ijecs.in Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2014) Going Deeper with Convolutions. CoRR, abs/1409.4842. http://arxiv.org/abs/1409.4842 Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826 Satish Tunga D, Jayadevappa D, Gururaj. C (2015) A comparative study of content based image retrieval trends and approaches. Int J Image Proc (IJIP) 9(3):127 Tzelepi M, Tefas A (2018) Deep convolutional learning for content based image retrieval. Neuro-computing 275:2467–2478 Vakhitov A, Kuzmin A, Lempitsky V (2016) Internet-based image retrieval using end-to-end trained deep distributions. arXiv preprint arXiv:1612.07697 Chen L, Zhang Y, Song ZL, Miao Z (2013) Automatic web services classification based on rough set theory. J Central South Univ 20:2708–2714 Pineda Lezama O, Gómez Dorta R (2017) Techniques of multivariate statistical analysis: an application for the Honduran banking sector. Innovare: J Sci Technol 5(2):61–75 Viloria A, Lis-Gutiérrez JP, Gaitán-Angulo M, Godoy ARM, Moreno GC, Kamatkar SJ (2018) Methodology for the design of a student pattern recognition tool to facilitate the teaching—learning process through knowledge data discovery (big data). In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Cham Zhu F et al (2009) IBM Cloud computing powering a smarter planet. Libro Cloud Computing, Volumen 599.51/2009, pp 621–625 Mohanty R, Ravi V, Patra MR (2010) Web-services classification using intelligent techniques. Expert Syst Appl 37(7):5484–5490 Thames L, Schaefer D (2016) Softwaredefined cloud manufacturing for industry 4.0. Procedía CIRP, 52:12–17 Viloria A, Neira-Rodado D, Lezama OBP (2019) Recovery of scientific data using intelligent distributed data warehouse. ANT/EDI40 1249–1254 Schweidel DA, Knox G (2013) Incorporating direct marketing activity into latent attrition models. Mark Sci 31(3):471–487 Setnes M, Kaymak U (2001) Fuzzy modeling of client preference from large data sets: an application to target selection in direct marketing. Fuzzy Syst, IEEE Trans 9(1):153–163 Viloria A, Lezama OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. ANT/EDI40 1201–1206 Nisa R, Qamar U (2014) A text mining based approach for web service classification. Inf Syst e-Business Manage 1–18 Wu J, Chen L, Zheng Z, Lyu MR, Wu Z (2014) Clustering web services to facilitate service discovery. Knowl Inf Syst 38(1):207–229 Alderson J (2015) A markerless motion capture technique for sport performance analysis and injury prevention: toward a big data, machine learning future. J Sci Med Sport 19:e79. https://ezproxy.cuc.edu.co:2067/10.1016/j.jsams.2015.12.192 Alcalá R, Alcalá-Fdez J, Herrera F (2007) A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection. IEEE Trans Fuzzy Syst 15(4):616–635 Elsaid A, Salem R, Abdul-Kader H (2017) A dynamic stakeholder classification and prioritization based on hybrid rough-fuzzy method. J Software Eng 11:143–159 Molina R, Calle FR, Gazzano JD, Petrino R, Lopez JCL (2019) Implementation of search process for a content-based image retrieval application on system on chip. In: 2019 X southern conference on programmable logic (SPL). IEEE, pp 97–102 Maur HK, Faridkot P, Jain, P (2019) Content based image retrieval system using K-means clustering algorithm and SVM classifier technique Pothoff WJ, Price TG, Prasolov V (2020) U.S. Patent No. 10,565,070. U.S. Patent and Trademark Office, Washington, DC Poplawska J, Labib A, Reed DM, Ishizaka A (2015) Stakeholder profile definition and salience measurement with fuzzy logic and visual analytics applied to corporate social responsibility case study. J Clean Prod 105:103–115. https://ezproxy.cuc.edu.co:2067/10.1016/j.jclepro.2014.10.095 Paulin M et al (2017) Convolutional patch representations for image retrieval: an unsupervised approach. Int J Comput Vis 165–166 Chandrasekhar V, Lin J, Liao Q, Morere O, Veillard A, Duan L, Poggio T (2017) Compression of deep neural networks for image instance retrieval. arXiv preprint arXiv:1701.04923 Zhang T, Qi G-J, Tang J, Wang J (2015) Sparse composite quantization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4548–4556 Gong Y, Wang L, Guo R, Lazebnik S (2014) Multi-scale orderless pooling of deep convolutional activation features. In: European conference on computer vision. Springer, pp 392–407 Jegou H, Perronnin F, Douze M, Sanchez J, Perez P, Schmid C (2012) Aggregating local image descriptors into compact codes. IEEE Trans Pattern Anal Mach Intell 34(9):1704–1716 Perronnin F, Larlus D (2015) Fisher vectors meet neural networks: a hybrid classification architecture. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3743–3752 Jegou H, Zisserman A (2014) Triangulation embedding and democratic aggregation for image search. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3310–3317 |
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amelec, viloriaVarela Izquierdo, NoelVargas, JesúsPineda, Omar2020-11-12T17:33:39Z2020-11-12T17:33:39Z20202021-01-312194-5357https://hdl.handle.net/11323/7276Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The techniques of content-based image recovery (CBIR) provide a solution to a problem of information retrieval that may arise as follows: from an image of interest to recover or obtain similar images from among those present in a large collection, using only features or features extracted from said images Banuchitra and Kungumaraj (Int J Eng Comput Sci (IJECS) 5 (2016) [1]). Similar images are understood as those in which the same object or scene is observed with variations in perspective, lighting conditions or scale. The stored images are preprocessed and then their corresponding descriptors are indexed. The query image is also preprocessed to extract its descriptor, which is then compared to those stored by applying appropriate similarity measures, which allow the recovery of those images that are similar to the query image. In the present work, a method was developed for the recovery of indexed images in databases from their visual content, without the need to make textual annotations. Feature vectors were obtained from visual contents using artificial neural network techniques with deep learning.amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Varela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Vargas, JesúsPineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbAdvances in Intelligent Systems and Computinghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85090096092&doi=10.1007%2f978-981-15-6876-3_40&partnerID=40&md5=b1a210aefb1d5c3ecab03113a88361a6Convolutional neural networksGlobal descriptorsImage retrievalInformation retrievalMethod for the recovery of indexed images in databases from visual contentPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersionBanuchitra S, Kungumaraj K (2016) A comprehensive survey of content based image retrieval techniques. Int J Eng Comput Sci (IJECS) 5 (2016). https://ezproxy.cuc.edu.co:2067/10.18535/ijecs/v5i8.26. https://www.ijecs.inSimonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2014) Going Deeper with Convolutions. CoRR, abs/1409.4842. http://arxiv.org/abs/1409.4842Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826Satish Tunga D, Jayadevappa D, Gururaj. C (2015) A comparative study of content based image retrieval trends and approaches. Int J Image Proc (IJIP) 9(3):127Tzelepi M, Tefas A (2018) Deep convolutional learning for content based image retrieval. Neuro-computing 275:2467–2478Vakhitov A, Kuzmin A, Lempitsky V (2016) Internet-based image retrieval using end-to-end trained deep distributions. arXiv preprint arXiv:1612.07697Chen L, Zhang Y, Song ZL, Miao Z (2013) Automatic web services classification based on rough set theory. J Central South Univ 20:2708–2714Pineda Lezama O, Gómez Dorta R (2017) Techniques of multivariate statistical analysis: an application for the Honduran banking sector. Innovare: J Sci Technol 5(2):61–75Viloria A, Lis-Gutiérrez JP, Gaitán-Angulo M, Godoy ARM, Moreno GC, Kamatkar SJ (2018) Methodology for the design of a student pattern recognition tool to facilitate the teaching—learning process through knowledge data discovery (big data). In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, ChamZhu F et al (2009) IBM Cloud computing powering a smarter planet. Libro Cloud Computing, Volumen 599.51/2009, pp 621–625Mohanty R, Ravi V, Patra MR (2010) Web-services classification using intelligent techniques. Expert Syst Appl 37(7):5484–5490Thames L, Schaefer D (2016) Softwaredefined cloud manufacturing for industry 4.0. Procedía CIRP, 52:12–17Viloria A, Neira-Rodado D, Lezama OBP (2019) Recovery of scientific data using intelligent distributed data warehouse. ANT/EDI40 1249–1254Schweidel DA, Knox G (2013) Incorporating direct marketing activity into latent attrition models. Mark Sci 31(3):471–487Setnes M, Kaymak U (2001) Fuzzy modeling of client preference from large data sets: an application to target selection in direct marketing. Fuzzy Syst, IEEE Trans 9(1):153–163Viloria A, Lezama OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. ANT/EDI40 1201–1206Nisa R, Qamar U (2014) A text mining based approach for web service classification. Inf Syst e-Business Manage 1–18Wu J, Chen L, Zheng Z, Lyu MR, Wu Z (2014) Clustering web services to facilitate service discovery. Knowl Inf Syst 38(1):207–229Alderson J (2015) A markerless motion capture technique for sport performance analysis and injury prevention: toward a big data, machine learning future. J Sci Med Sport 19:e79. https://ezproxy.cuc.edu.co:2067/10.1016/j.jsams.2015.12.192Alcalá R, Alcalá-Fdez J, Herrera F (2007) A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection. IEEE Trans Fuzzy Syst 15(4):616–635Elsaid A, Salem R, Abdul-Kader H (2017) A dynamic stakeholder classification and prioritization based on hybrid rough-fuzzy method. J Software Eng 11:143–159Molina R, Calle FR, Gazzano JD, Petrino R, Lopez JCL (2019) Implementation of search process for a content-based image retrieval application on system on chip. In: 2019 X southern conference on programmable logic (SPL). IEEE, pp 97–102Maur HK, Faridkot P, Jain, P (2019) Content based image retrieval system using K-means clustering algorithm and SVM classifier techniquePothoff WJ, Price TG, Prasolov V (2020) U.S. Patent No. 10,565,070. U.S. Patent and Trademark Office, Washington, DCPoplawska J, Labib A, Reed DM, Ishizaka A (2015) Stakeholder profile definition and salience measurement with fuzzy logic and visual analytics applied to corporate social responsibility case study. J Clean Prod 105:103–115. https://ezproxy.cuc.edu.co:2067/10.1016/j.jclepro.2014.10.095Paulin M et al (2017) Convolutional patch representations for image retrieval: an unsupervised approach. Int J Comput Vis 165–166Chandrasekhar V, Lin J, Liao Q, Morere O, Veillard A, Duan L, Poggio T (2017) Compression of deep neural networks for image instance retrieval. arXiv preprint arXiv:1701.04923Zhang T, Qi G-J, Tang J, Wang J (2015) Sparse composite quantization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4548–4556Gong Y, Wang L, Guo R, Lazebnik S (2014) Multi-scale orderless pooling of deep convolutional activation features. In: European conference on computer vision. Springer, pp 392–407Jegou H, Perronnin F, Douze M, Sanchez J, Perez P, Schmid C (2012) Aggregating local image descriptors into compact codes. IEEE Trans Pattern Anal Mach Intell 34(9):1704–1716Perronnin F, Larlus D (2015) Fisher vectors meet neural networks: a hybrid classification architecture. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3743–3752Jegou H, Zisserman A (2014) Triangulation embedding and democratic aggregation for image search. 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