BOVW For Classification In Geometrics Shapes

• Autorzy: Zurita Baldemar , Luna Luís , Hernández José , Ramírez Federico

Identyfikatory

DOI

10.23743/acs-2018-25

• Języki publikacji: EN

Abstrakty

EN

The classification of forms is a process used in various areas, to perform a classification based on the manipulation of shape contours it is necessary to extract certain common characteristics, it is proposed to use the bag of visual words model, this method consists of three phases: detection and extraction of characteristics, representation of the image and finally the classification. In the first phase of detection and extraction the SIFT and SURF methods will be used, later in the second phase a dictionary of words will be created through a process of clustering using K-means, EM, K-means in combination with EM, finally in the Classification will be compared algorithms of SVM, Bayes, KNN, RF, DT, AdaBoost, NN, to determine the performance and accuracy of the proposed method.

Słowa kluczowe

EN

BOVW; classification; codebook

PL

BOVW; klasyfikacja; słownik kodów

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2018
• Tom: Vol. 14, no 4
• Strony: 5--11
• Opis fizyczny: Bibliogr. 8 poz., fig., tab.

Twórcy

autor

Zurita Baldemar

• Apizaco Technological Institute, Computer Systems Department, Apizaco, Tlaxcala, Mexico

autor

Luna Luís

• Apizaco Technological Institute, Computer Systems Department, Apizaco, Tlaxcala, Mexico

autor

Hernández José

• Apizaco Technological Institute, Computer Systems Department, Apizaco, Tlaxcala, Mexico

autor

Ramírez Federico

• Apizaco Technological Institute, Departament of Computer and Systems, Apizaco, Tlaxcala, Mexico

Bibliografia

• [1] Ben Hamza, A. (2016). A graph-theoretic approach to 3D shape classification. Neurocomputing, 211, 11–21.
• [2] Jia, Q., Fan, X., Liu, Y., Li, H., Luo, Z., & Guo, H. (2016). Hierarchical projective invariant contexts for shape recognition. Pattern Recognition, 52, 358–374. doi:10.1016/J.PATCOG. 2015.11.003
• [3] Li, C., & Ben Hamza, A. (2014). Spatially aggregating spectral descriptors for nonrigid 3D shape retrieval: a comparative survey. Multimedia Systems, 20(3), 253–281. doi:10.1007/s00530-013-0318-0
• [4] Shaban, A., Rabiee, H., Farajtabar, M., & Ghazvininejad M. (2013). From local similarity to global coding; an application to image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (pp. 2794–2801). Portland, USA: IEEE.
• [5] Sivic, J., & Zisserman, A. (2003). Video Google: a text retrieval approach to object matching in videos. In: Proceedings of the Ninth IEEE International Conference on Computer Vision – Volume 2 (pp. 1–9). USA: IEEE Computer Society Washington.
• [6] Szelinski, R. (2011). Computer Vision: Algorithms and Applications (pp. 658–729). Springer Verlag.
• [7] Wang, X., Feng, B., Bai, X., Liu, W., & Latecki, L. J. (2014). Bag of contour fragments for robust shape classification. Pattern Recognition, 47(6), 2116–2125.
• [8] Ye, J., & Yu, Y. (2016). A fast modal space transform for robust nonrigid shape retrieval. The Visual Computer, 32(5), 553–568. doi:10.1007/s00371-015-1071-5