BOVW FOR CLASSIFICATION IN GEOMETRICS SHAPES
Baldemar ZURITA
baldemar.zurita@gmail.comApizaco Technological Institute, Computer Systems Department, Apizaco, Tlaxcala (Mexico)
Luís LUNA
Apizaco Technological Institute, Computer Systems Department, Apizaco, Tlaxcala (Mexico)
José HERNÁNDEZ
* Apizaco Technological Institute, Computer Systems Department, Apizaco, Tlaxcala (Mexico)
Federico RAMÍREZ
Apizaco Technological Institute, Departament of Computer and Systems, Apizaco, Tlaxcala (Mexico)
Abstract
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.
Keywords:
BOVW, classification, codebookReferences
Ben Hamza, A. (2016). A graph-theoretic approach to 3D shape classification. Neurocomputing, 211, 11–21.
DOI: https://doi.org/10.1016/j.neucom.2015.12.130
Google Scholar
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. https://doi.org/10.1016/J.PATCOG. 2015.11.003
DOI: https://doi.org/10.1016/j.patcog.2015.11.003
Google Scholar
Li, C., & Ben Hamza, A. (2014). Spatially aggregating spectral descriptors for nonrigid 3D shape retrieval: a comparative survey. Multimedia Systems, 20(3), 253–281. https://doi.org/10.1007/s00530-013-0318-0
DOI: https://doi.org/10.1007/s00530-013-0318-0
Google Scholar
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.
DOI: https://doi.org/10.1109/CVPR.2013.360
Google Scholar
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.
DOI: https://doi.org/10.1109/ICCV.2003.1238663
Google Scholar
Szelinski, R. (2011). Computer Vision: Algorithms and Applications (pp. 658–729). Springer Verlag.
Google Scholar
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.
DOI: https://doi.org/10.1016/j.patcog.2013.12.008
Google Scholar
Ye, J., & Yu, Y. (2016). A fast modal space transform for robust nonrigid shape retrieval. The Visual Computer, 32(5), 553–568. https://doi.org/10.1007/s00371-015-1071-5
DOI: https://doi.org/10.1007/s00371-015-1071-5
Google Scholar
Authors
Baldemar ZURITAbaldemar.zurita@gmail.com
Apizaco Technological Institute, Computer Systems Department, Apizaco, Tlaxcala Mexico
Authors
Luís LUNAApizaco Technological Institute, Computer Systems Department, Apizaco, Tlaxcala Mexico
Authors
José HERNÁNDEZ* Apizaco Technological Institute, Computer Systems Department, Apizaco, Tlaxcala Mexico
Authors
Federico RAMÍREZApizaco Technological Institute, Departament of Computer and Systems, Apizaco, Tlaxcala Mexico
Statistics
Abstract views: 110PDF downloads: 22
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Sheikh Amir FAYAZ, Majid ZAMAN, Muheet Ahmed BUTT, Sameer KAUL, HOW MACHINE LEARNING ALGORITHMS ARE USED IN METEOROLOGICAL DATA CLASSIFICATION: A COMPARATIVE APPROACH BETWEEN DT, LMT, M5-MT, GRADIENT BOOSTING AND GWLM-NARX MODELS , Applied Computer Science: Vol. 18 No. 4 (2022)
- Mouna TARIK, Ayoub MNIAI, Khalid JEBARI, HYBRID FEATURE SELECTION AND SUPPORT VECTOR MACHINE FRAMEWORK FOR PREDICTING MAINTENANCE FAILURES , Applied Computer Science: Vol. 19 No. 2 (2023)
You may also start an advanced similarity search for this article.
