Citra Tekstur Terbaik Untuk Gaussian Naïve Bayes Dengan Interpolasi Nearest Neighbor
Abstrak
Salah satu faktor yang berpengaruh terhadap kinerja Gaussian naive Bayes classifier (GNBC) dalam klasifikasi citra tekstur adalah ukuran (dimensi) citra. Ukuran citra adalah salah satu kriteria citra tekstur terbaik di samping nilai pikselnya. Pada penelitian ini, diusulkan metode untuk mendapatkan ukuran citra tekstur terbaik terhadap GNBC dengan optimalisasi interpolasi nearest neighbor (NN). Ukuran citra tekstur terbaik dengan nilai piksel hasil interpolasi tersebut membuat GNBC mampu membedakan citra tekstur pada setiap kelasnya dengan kinerja paling tinggi. Langkah pertama metode usulan tersebut adalah menentukan ukuran citra tekstur untuk pelatihan melalui kombinasi ukuran baris dan kolom pada proses optimalisasi. Pengubahan ukuran (resizing) semua citra tekstur asal dengan interpolasi NN adalah langkah penting berikutnya untuk mendapatkan citra tekstur baru. Langkah selanjutnya adalah membangun GNBC berdasarkan citra baru hasil interpolasi dan menentukan akurasi klasifikasinya. Langkah terakhir yaitu memilih ukuran citra tekstur terbaik berdasarkan nilai akurasi klasifikasi terbesar sebagai kriteria pertama dan ukuran citra sebagai kriteria kedua. Evaluasi terhadap metode yang diusulkan tersebut dilakukan menggunakan data citra tekstur dari dataset publik CVonline dengan melibatkan beberapa skenario uji coba dan metode interpolasi. Hasil uji coba menunjukkan bahwa pada skenario yang melibatkan lima kelas citra tekstur, GNBC dengan interpolasi NN memberikan nilai akurasi klasifikasi terkecil 89% dan terbesar 100% pada ukuran citra terbaik, masing-masing 14 × 32 dan 47 × 42. Pada skenario yang melibatkan jumlah kelas kecil hingga besar, GNBC dengan interpolasi NN memberikan akurasi klasifikasi 81,6% - 95%. Dari hasil tersebut, GNBC dengan optimalisasi NN memberikan hasil lebih baik daripada metode interpolasi nonadaptif lainnya (bilinear, bicubic, dan lanczos) dan principal component analysis (PCA).
Referensi
M.S. Nixon and A.S. Aguado, Feature Extraction and Image Processing, 1st ed. Oxford, England: Newnes, 2002.
Y.-H. Shin, M.-J. Park, O.-Y. Lee, and J.-O. Kim, “Deep orthogonal transform feature for image denoising,” IEEE Access, vol. 8, pp. 66898–66909, Apr. 2020, doi: 10.1109/ACCESS.2020.2986827.
I.T. Jolliffe, Principal Component Analysis, 2nd ed. New York, NY, USA: Springer-Verlag, 2002.
S.H. Mahajan and V.K. Harpale, “Adaptive and non-adaptive image interpolation techniques,” 2015 Int. Conf. Comput. Commun. Control Automat., 2015, pp. 772–775, doi: 10.1109/ICCUBEA.2015.154.
G. Ramesh and T.A. Prasath, “An aphoristic study on different interpolation techniques for medical image scaling and its comparative analysis,” 2021 Int. Conf. Comput. Commun. Inform. (ICCCI), 2021, pp. 1–4, doi: 10.1109/ICCCI50826.2021.9402675.
D. újica-Vargas, Y. Mújica-Vargas, M.M. Cruz, and A. Rendón-Castro, “Improvement of MRI images through heterogeneous interpolation techniques,” 2019 Int. Conf. Electron. Commun. Comput. (CONIELECOMP), 2019, pp. 112–117, doi: 10.1109/CONIELECOMP.2019.8673230.
P. Bhatt, S. Patel, and R. Pandit, “Comparative analysis of interpolation and texture synthesis method for enhancing image,” Int. J. Innov. Res. Sci. Eng. Technol., vol. 2, no.1, pp. 278–283, Jan. 2013.
S. Safinaz and A.V.R. Kumar, “VLSI realization of Lanczos interpolation for a generic video scaling algorithm,” 2017 Int. Conf. Recent Adv. Electron. Commun. Technol. (ICRAECT), 2017, pp. 17–23, doi: 10.1109/ICRAECT.2017.37.
I.B. Santoso, Supriyono, C. Crysdian, and K.F.H. Holle, “Optimization of naïve Bayes classifier to classify green open space object based on Google Earth image,” 2018 Int. Seminar Res. Inf. Technol. Intell. Syst. (ISRITI), 2018, pp. 465–469, doi: 10.1109/ISRITI.2018.8864279.
P. Domingos and M. Pazzani, “On the optimality of the simple Bayesian classifier under zero-one loss,” Mach. Learn., vol. 29, pp. 103–130, Nov. 1997, doi: 10.1023/A:1007413511361.
G.N. Norén and R. Orre, “Case based imprecision estimates for Bayes classifiers with the Bayesian bootstrap,” Mach. Learn., vol. 58, pp. 79–94, Jan. 2005, doi: 10.1007/s10994-005-5010-y.
M. Ekdahl and T. Koski, “Bounds for the loss in probability of correct classification under model based approximation,” J. Mach. Learn. Res., vol. 7, pp. 2449–2480, Nov. 2006.
M. Hall, “A decision tree-based attribute weighting filter for naive Bayes,” Knowl.-Based Syst., vol. 20, no. 2, pp. 120–126, Mar. 2007, doi: 10.1016/j.knosys.2006.11.008.
T.-T. Wong, “Alternative prior assumptions for improving the performance of naïve Bayesian classifiers,” Data Min. Knowl. Discov., vol. 18, no. 2, pp. 183–213, Apr. 2009, doi: 10.1007/s10618-008-0101-6.
G. Shobha and S. Rangaswamy, “Machine learning,” in Handbook of Statistics 48: Deep Learning, V. Gavindaraju, A.S.R.S. Rao, and C.R. Rao, Eds., Cambridge, USA: Academic Press Publications, 2018, pp. 197–228, doi: 10.1016/bs.host.2018.07.004.
X. Wu et al., “Top 10 algorithms in data mining,” Knowl. Inf. Syst., vol. 14, no. 1, pp. 1–37, Jan. 2008, doi: 10.1007/s10115-007-0114-2.
A.R. Webb and K.D. Copsey, Statistical Pattern Recognition, 3rd ed. Hoboken, USA: John Wiley & Sons, Ltd., 2011.
R.C. Gonzalez, R.E. Woods, and S.L. Eddins, Digital Image Processing Using MATLAB, 2nd ed. Knoxville, USA: Gatesmark Publishing, 2009.
S. Iqbal et al., “Prostate cancer detection using deep learning and traditional techniques,” IEEE Access, vol. 9, pp. 27085–27100, Feb. 2021, doi: 10.1109/ACCESS.2021.3057654.
S. Lazebnik, C. Schmid, and J. Ponce (2003) The texture database, CVonline image database. [Online], http://www-cvr.ai.uiuc.edu/ponce_grp/data/#texture, access date: 18-Apr-2017.
D.M.W. Powers, “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation,” 2020, arXiv.2010.16061.
T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett., vol. 27, no. 8, pp. 861–874, Jun. 2006, doi: 10.1016/j.patrec.2005.10.010.
W. Zhao, Y. Lv, Q. Liu, and B. Qin, “Detail-preserving image denoising via adaptive clustering and progressive PCA thresholding,” IEEE Access, vol. 6, pp. 6303–6315, Dec. 2017, doi: 10.1109/ACCESS.2017.2780985.
T. Acharya and P.-S. Tsai, “Computational foundations of image interpolation algorithms,” Ubiquity, vol. 2007, no. October, pp. 1–17, Oct. 2007, doi: 10.1145/1322464.1317488.
Z. Xingyu, W. Yong, and L. Xiaofei, “Approach for ISAR imaging of near-field targets based on coordinate conversion and image interpolation,” J. Syst. Eng. Electron., vol. 32, no. 2, pp. 425–436, Apr. 2021, doi: 10.23919/JSEE.2021.000036.
K.-L. Chung, C.-Y. Huang, and C.-W. Kao, “An effective bicubic convolution interpolation-based iterative luma optimization for enhancing quality in chroma subsampling,” IEEE Access, vol. 9, pp. 149744–149755, Nov. 2021, doi: 10.1109/ACCESS.2021.3125713.
C.-S. Tsai, H.-H. Liu, and M.-C. Tsai, “Design of a scan converter using the cubic convolution interpolation with Canny edge detection,” 2011 Int. Conf. Elect. Inf. Control Eng., 2011, pp. 5813–5816, doi: 10.1109/ICEICE.2011.5777979.
R.V. Sharan and T.J. Moir, “Time-frequency image resizing using interpolation for scoustic event recognition with convolutional neural networks,” 2019 IEEE Int. Conf. Signals Syst. (ICSigSys), 2019, pp. 8–11, doi: 10.1109/ICSIGSYS.2019.8811088.
A. Puziy, I. Gavrilov, K. Nosirov, and A. Akhmedova, “Efficiency estimation of image resizing based on interpolating transformations,” 2019 Int. Conf. Inf. Sci. Commun. Technol. (ICISCT), 2019, pp. 1-5, doi: 10.1109/ICISCT47635.2019.9011885.
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