Técnicas y usos en la clasificación automática de imágenes

Gil-Leiva, Isidoro, Díaz-Ortuño, Pedro-Manuel and Rodríguez-Muñoz, José-Vicente Técnicas y usos en la clasificación automática de imágenes., 2019 (Submitted) [Preprint]

[thumbnail of Texto en Español]
Preview
Text (Texto en Español)
Clasificacion imagenes ISKO2019.pdf - Submitted version

Download (846kB) | Preview

English abstract

The production and generation of visual information through mobile phones and cameras is enormous. Also and mainly through remote sensing, through the acquisition of images of the earth's surface by means of planes, spacecraft and satellites that capture and serve data on meteorology, oceanography, geology, geography, geolocation, security, and so on. These image capture instruments generate visual information every day that cannot be manually processed, which is why various techniques and methods are used for the automatic extraction of useful knowledge. This literature review aims to understand the techniques and uses of automatic classification of images. In order to do this, the Scopus and WoS databases were used to locate documents on the automatic classification of images published between 2008 and 2018. The resulting records were searched for their full texts, carrying out a content analysis to find out the most recurrent techniques and their applications. As a result, it becomes evident that the three most commonly used techniques for the automatic classification of images are decision trees, neural networks and support vector machines, with the application of a wide variety of automatic classification, which seeks to automate repetitive processes, inspection and complex surveillance, urban control and development or recognition and assessment after natural disasters, among other aspects.

Spanish abstract

La producción y generación de información visual mediante teléfonos móviles y cámaras es ingente. También y principalmente a través de la teledetección, mediante la obtención de imágenes de la superficie terrestre por medio de aviones, naves espaciales y satélites que captan y sirven datos sobre meteorología, oceanografía, geología, geografía, geolocalización, seguridad, etc. Estos instrumentos de captura de imágenes generan cada día información visual imposible de procesar manualmente, de ahí que se recurra a diversas técnicas y métodos para la extracción automática de conocimientos útiles. Esta revisión bibliográfica, pretende conocer las técnicas y usos de la clasificación automática de imágenes. Para ello, se emplearon las Bases de datos Scopus y WoS para localizar documentos sobre clasificación automática de imágenes publicados entre 2008 y 2018. De los registros resultantes se buscaron los textos completos de los mismos, llevando a cabo un análisis del contenido para averiguar las técnicas más recurrentes y sus aplicaciones. Con todo ello, se hace patente que las tres técnicas más usadas para la clasificación automática de imágenes son los árboles de decisiones, redes neuronales y máquinas de vectores de soporte, siendo la aplicación de la clasificación automática muy variada, con la que se buscan automatizar procesos repetitivos, la inspección y vigilancias complejas, el control y desarrollo urbanístico o el reconocimiento y valoración tras catástrofes naturales, entre otros asuntos.

Item type: Preprint
Keywords: Automatic classification of images; techniques and applications; literature review. Clasificación automática de imágenes; técnicas y aplicaciones; revisión bibliográfica.
Subjects: H. Information sources, supports, channels. > HH. Audio-visual, Multimedia.
I. Information treatment for information services > IB. Content analysis (A and I, class.)
Depositing user: Pedro Díaz
Date deposited: 12 Jul 2019 09:49
Last modified: 12 Jul 2019 09:49
URI: http://hdl.handle.net/10760/38798

References

Al-Batah, M. S., Isa, N. A. M., Zamli, K. Z., Sani, Z. M., y Azizli, K. A. (2009). A novel aggregate classification technique using moment invariants and cascaded multilayered perceptron network. International Journal of Mineral Processing, 92(1-2), 92–102.

Amutha, A. L., y Kavitha, S. (2011). Features based classification of images using weighted feature support vector machines. Int J Comput Appl, 26(10), 23–9.

Augereau, O., Journet, N., Vialard, A., y Domenger, J.-P. (2014). Improving classification of an industrial document image database by combining visual and textual features. En Document Analysis Systems (DAS), 2014 11th IAPR International Workshop on (pp. 314–318). IEEE.

Cintra, D. P., Novack, T., Rego, L. F. G., Costa, G., y Feitosa, R. Q. (2010). PIMAR ProjectMonitoring the atlantic rainforest remnants and the urban growth of the Rio de Janeiro city (Brazil) through remote sensing. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-4/C7.

Correia, R., Duarte, L., Teodoro, A., y Monteiro, A. (2018). Processing Image to Geographical Information Systems (PI2GIS)—A Learning Tool for QGIS. Education Sciences, 8(2), 83.

Das, A.J. Saikia, N. y Sarma, K.K. (2016). Object classification and tracking in real time: an overview. P. 250-295. En Santhi, V., Acharjya, D.P. y Ezhilarasan, M. (eds.). Emerging technologies in intelligent applications for image and video processing. Hershey, PA: IGI Global.

Dimitrios, A., Rousopoulos, P., Papaodysseus, C., Panagopoulos, M., Loumou, P., y Theodoropoulos, G. (2010). A general methodology for the determination of 2D bodies elastic deformation invariants: Application to the automatic identification of parasites. IEEE transactions on pattern analysis and machine intelligence, 32(5), 799–814.

Ghaffarian, S. (2014). Automatic building detection based on supervised classification using high resolution Google Earth images. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(3), 101.

HaCohen-Kerner, Y., Sabag, A., Liparas, D., Moumtzidou, A., Vrochidis, S., y Kompatsiaris, I. (2015). Classification Using Various Machine Learning Methods and Combinations of Key-Phrases and Visual Features. En Semanitic Keyword-based Search on Structured Data Sources (pp. 64–75). Springer.

Hao Z., Ge H. y Wang, L. (2018) Visual attention mechanism and support vector machine based automatic image annotation. PLoS ONE, 13(11).

Hemsley, A., y Mukundan, R. (2009). Multifractal Measures for Tissue Image Classification and Retrieval. 11th IEEE International Symposium on Multimedia(ISM), San Diego, California, pp. 618-623.

Hermosilla, T., Ruiz, L. A., Recio, J. A., y Estornell, J. (2011). Evaluation of automatic building detection approaches combining high resolution images and LiDAR data. Remote Sensing, 3(6), 1188–1210.

Hughes, N. (2009). Sea ice type classification from multichannel passive microwave datasets. En Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009 (Vol. 3, pp. III–125). IEEE.

Jabari, S. y Zhang, Y. (2013). Very high resolution satellite image classification using fuzzy rulebased Systems. Algorithms, 6, 762-781.

Jain, S. (2013). A machine learning approach: Svm for image classification in cbir. Internationa Journal of Aplication or Annovation in Engineering & Management (IJAIEM), 2(4).

Kotsiantis, S.B. (2007). Supervised machine learning: A review of classification techniques. Informatica, 31, 249-268.

Kupidura, P., Osińska-Skotak, K., y Pluto-Kossakowska, J. (2016). Automatic Approach to Vhr Satellite Image Classification. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 277–282.

Li, B., Tan, X., Wang, F., Lian, P., Gao, W., y Li, Y. (2017). Fracture and vug characterization and carbonate rock type automatic classification using X-ray CT images. Journal of Petroleum Science and Engineering, 153, 88–96.

Lin, Y., Li, W. J., Yu, J., y Wu, C. Z. (2018). Ecological Sensitivity Evaluation of Tourist Region Based on Remote Sensing Image–Taking Chaohu Lake Area as a Case Study. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42(3).

Ma, L, Zeheng, G., Eitel, J. y Moskal, L.M. (2016). Improved Salient Feature-Based Approach for Automatically Separating Photosynthetic and Nonphotosynthetic Components Within Terrestrial Lidar Point Cloud Data of Forest Canopies. IEEE Transactions on geoscience and remote sensing, 54(2), 679-696.

Minetto, R. Thome, N., Cord, M., Leite, N.J. y Stolfi, J. (2014). SnooperText: A text detection system for automatic indexing of urban scenes. Computer Vision and Image Understanding, 122, 92-104.

Morioka, C., Meng, F., Taira, R., Sayre, J., Zimmerman, P., Ishimitsu, D. El-Saden, S. (2016). Automatic classification of ultrasound screening examinations of the abdominal aorta. Journal of digital imaging, 29(6), 742–748.

Murphy, J. M., y Maggioni, M. (2018). Unsupervised Clustering and Active Learning of Hyperspectral Images With Nonlinear Diffusion. IEEE Transactions on Geoscience and Remote Sensing.

Ping Tian, D. (2013). A review on image feature extraction and representation techniques. International Journal of Multimedia and Ubiquitous Engineering, 8(4), 385–396.

Raj, K. J., y SivaSathya, S. (2016). A Survey of Various Algorithms Used on Multispectral Satellite Image Classification of Alwar Image Dataset. Indian Journal of Science and Technology, 9(45).

Rezaeian, M. (2012). Automatic classification of collapsed buildings using stereo aerial images. International Journal of Computer Applications, 46(21), 35–42.

Senthilnath, J., Kulkarni, S., Benediktsson, J. A., y Yang, X.-S. (2016). A novel approach for multispectral satellite image classification based on the bat algorithm. IEEE Geoscience and Remote Sensing Letters, 13(4), 599–603.

Shen, J. (2009). Stochastic modeling western paintings for effective classification. Pattern Recognition. 42(2), 293-301.

Tian, Dong Ping (2013). A Review on image feature extraction and representation techniques. International Journal of Multimedia and Ubiquitous Engineering, 8(4), 385-396.

Velazco Paredes, Y.E. (2014). Recuperación de imágenes por contenido basado en regiones con retroalimentación por relevancia. Universidad Nacional de San Agustín de Arequipa. Tesis doctoral. Disponible en : http://repositorio.unsa.edu.pe/handle/UNSA/6129 [Consultado: 05-11-2018].

Yang, M. D., Huang, K. S., Kuo, Y. H., Tsai, H. P., & Lin, L. M. (2017). Spatial and spectral hybrid image classification for rice lodging assessment through UAV imagery. Remote Sensing, 9(6), 583.

You, Y., Wang, S., Ma, Y., Chen, G., Wang, B., Shen, M., y Liu, W. (2018). Building detection from VHR remote sensing imagery based on the morphological building index. Remote Sensing, 10(8), 1287. doi: 10.3390/rs10081287

Zhang, F. y Zhang, X. (2011). Classification and quality evaluation of tobacco leaves based on image processing and fuzzy comprehensive evaluation. Sensors, 11, 2369-2384.

Zhang, W., Hu, B., Jing, L., Woods, M. E., y Courville, P. (2008). Automatic forest species classification using combined LIDAR data and optical imagery. En Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International (Vol. 3, pp. III–134). IEEE.


Downloads

Downloads per month over past year

Actions (login required)

View Item View Item