Butterfly Image Classification Using Color Quantization Method on HSV Color Space and Local Binary Pattern

Dhian Satria Yudha Kartika, Darlis Herumurti, Anny Yuniarti

Abstract


A lot of methods are used to develop on image research. Image detection to relay back new information, widely used in various research field, such as health, agriculture or other field research. Various methods are used and developed to get better results. A combination of several methods is performed for testing as part of the research contribution. In this study will perform the combination results of the process color feature extraction with texture features. In color feature extraction using HSV color space method that gets 72 feature extraction and on texture feature extraction using local binary pattern that gets 256 feature extraction. The process of merging the two extracted results gets 328 new feature extractions. The result of combining color feature extraction and texture feature extraction is further classified. Results from image classification of butterflies get an accuracy score of 72%. The results obtained will be tested performance. The results obtained from performance testing get precision value, recall and f-measure respectively 76%, 72% and 74%

Keywords


Color Feature, Texture Feature, Feature Extraction; Image Processing; Classification

Full Text:

PDF

References


A. A. Yunanto and D. Herumurti, “Face recognition based on Extended Symmetric Local Graph Structure,” in 2016 International Conference on Information & Communication Technology and Systems (ICTS), 2016, pp. 80–84.

W. A. Saputra and D. Herumurti, “Integration GLCM and geometric feature extraction of region of interest for classifying tuna,” in 2016 International Conference on Information & Communication Technology and Systems (ICTS), 2016, pp. 75–79.

Y. Kaya, L. Kayci, and M. Uyar, “Automatic identification of butterfly species based on local binary patterns and artificial neural network,” Appl. Soft Comput., vol. 28, pp. 132–137, Mar. 2015.

Y. Kaya, L. Kayci, and R. Tekin, “A Computer Vision System for the Automatic Identification of Butterfly Species via Gabor- Filter-Based Texture Features and Extreme Learning Machine: GF+ELM,” TEM J. , vol. 2, no. 1, pp. 13–20.

L. Kayci and Y. Kaya, “A vision system for automatic identification of butterfly species using a grey-level co-occurrence matrix and multinomial logistic regression,” Zool. Middle East, vol. 60, no. 1, pp. 57–64, Jan. 2014.

Y. Kaya, L. Kayci, R. Tekin, and Ö. Faruk Ertuğrul, “Evaluation of texture features for automatic detecting butterfly species using extreme learning machine,” J. Exp. Theor. Artif. Intell., vol. 26, no. 2, pp. 267–281, Apr. 2014.

T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, Jul. 2002.

K. Burçin and N. V. Vasif, “Down syndrome recognition using local binary patterns and statistical evaluation of the system,” Expert Syst. Appl., vol. 38, no. 7, pp. 8690–8695, Jul. 2011.

D. S. Y. Kartika and D. Herumurti, “Koi fish classification based on HSV color space,” in 2016 International Conference on Information

& Communication Technology and Systems (ICTS), 2016, pp. 96–100.

N. Suciati, A. Kridanto, M. F. Naufal, M. Machmud, and A. Y. Wicaksono, “Fast discrete curvelet transform and HSV color features for batik image clansificotlon,” in 2015 International Conference on Information & Communication Technology and Systems (ICTS), 2015, pp. 99–104.

S. M. Youssef, “ICTEDCT-CBIR: Integrating curvelet transform with enhanced dominant colors extraction and texture analysis for efficient content-based image retrieval,” Comput. Electr. Eng., vol. 38, no. 5, pp. 1358–1376, Sep. 2012.

J. Wang, K. Markert, and M. Everingham, “Learning Models for Object Recognition from Natural Language Descriptions,” in Procedings of the British Machine Vision Conference 2009, 2009, p. 2.1-2.11.

A. Kurniawardhani, N. Suciati, and I. Arieshanti, “Klafisikasi Citra Batik Menggunakan Metode Ekstraksi Ciri yang Invariant Terhadap Rotasi,” JUTI J. Ilm. Teknol. Inf., vol. 12, no. 2, p. 48, Jul. 2014.

T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognit., vol. 29, no. 1, pp. 51–59, Jan. 1996.




DOI: http://dx.doi.org/10.12962/j23546026.y2018i1.3512

Refbacks

  • There are currently no refbacks.


View my Stat: Click Here

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.