PENGUKURAN KEMIRIPAN FITUR PADA SISTEM TEMU KEMBALI CITRA BERBASIS KONTEN MENGGUNAKAN EUCLIDIAN DISTANCE
Universitas Gunadarma
Indonesia
Universitas Gunadarma
Indonesia
Universitas Gunadarma
Indonesia
Article Submitted: 16 March 2022
Article Published: 10 May 2022
Abstract
Sistem Temu Kembali Citra berbasis Konten atau sistem, sudah diterapkan pada beberapa mesin pencari seperti Google dan Bing, tetapi citra hasil pencarian yang diberikan masih ada yang tidak relevan dengan citra permintaan. Membangun Temu Kembali Citra berbasis Konten yang dapat memberikan hasil pencarian yang relevan tergantung pada penarikan informasi dari konten citra yang dimasukan. Proses penarikan informasi terhadap konten suatu citra dapat dilakukan dengan menggunakan metode ekstraksi fitur berdasarkan konten warna, bentuk atau tekstur. Penelitian ini, mengukur jarak kesamaan atau kemiripan antara citra query dengan citra pada database menggunakan Euclidian Distance pada Sistem Temu Kembali Citra berbasis Konten berdasarkan warna dan tekstur. Ekstraksi fitur warna dilakukan menggunakan metode Momen Warna, dan fitur tekstur menggunakan Filter Gabor. Persentase presisi tingkat keberhasilan Sistem yang diuji pada setiap kategori menggunakan pengujian secara visual dengan memperhatikan citra groundtruth. Hasil terendah memiliki presisi sejumlah 50% pada kategori gunung dan presisi tertinggi sejumlah 100% pada kategori dinosaurus. Rata-rata persentase presisi tingkat keberhasilan Sistem Temu Kembali Citra berbasis Konten sejumlah 84% dari 10 data uji yang diambil dari database. Hasil yang diharapkan dari penelitian, aplikasi dapat mengidentifikasi citra berdasarkan ekstraksi fitur yang digunakan dan dapat menampilkan 10 citra yang mirip dengan citra query pada perangkat desktop.
Keywords
References
M. K. Ahirwal, A. Kumar, and G. K. Singh, “An approach to design self assisted CBIR system”, Proc. Int. Conf. Graph. Signal Process. – ICGSP, Vol. 17, pp.21–25, 2017.
K. T. Ahmed, S. Ummesafi, and A. Iqbal, “Content based image retrieval using image features information fusion”, Information Fusion, vol. 51, pp.76–99, 2019.
M. K. Alsmadi, “An efficient similarity measure for content based image retrieval using memetic algorithm”, Egyptian Journal of Basic and Applied Sciences, vol. 4, no. 2, pp.112–122, 2017.
M. K. Alsmadi, “Query-sensitive similarity measure for content-based image retrieval using meta-heuristic algorithm”, Journal of King Saud University-Computer and Inf. Sci., vol. 30, no. 3, pp.373–381, 2018.
M. Bouchakwa, Y. Ayadi, and I. Amous “Multi-level diversification approach of semantic-based image retrieval results”, Progress in Artificial Intelligence, vol. 9, no. 1, pp.1–30, 2020.
A. Du, L. Wang, and J. Qin, “Image retrieval based on colour and improved NMI texture features”, Automatika, vol. 60, no. 4, pp.491–499, 2019.
M. A. Aziz, A. A. Ewees, and A. E. Hassanien, “Multi-objective whale optimization algorithm for content-based image retrieval”, Multimed. Tools Appl., vol. 77, no. 19, pp.26135–26172, 2018.
D. A. Makandar, R. Somshekhar, and N. Jadav, “Content based image retrieval”, World Applied Sciences Journal, 19(3), 404–412. https://doi.org/10.5829/idosi.wasj.2012.19.03.1506, 2019.
R. Rajkumar, and M. V. Sudhamani, “Content based Image Retrieval System using Combination of Color and Shape Features, and Siamese Neural Network”, International Journal of Innovative Technology and Exploring Engineering, 9(2S), 71–77. https://doi.org/10.35940/ijitee.b1053.1292s19, 2019.
T. N. Phalke, and A.Patil, “Content Based Image Retrieval Using Color And Texture”, 9(1), 992–1000. https://doi.org/10.5121/sipij.2012.3104, 2017.
N. Varish, and A. K. Pal, “A content based image retrieval using color and texture features”, ACM International Conference Proceeding Series, 12-13. https://doi.org/10.1145/2979779.2979787, 2016.
B. Babu, R. Vanitha, and K. S. Anish, “Content based image retrieval using color, texture, shape and active re-ranking method”, Indian Journal of Science and Technology, 9(17). https://doi.org/10.17485/ijst/2016/v9i17/93107, 2016.
J. Li, and J. Z. Wang, “Automatic linguistic indexing of pictures by a statistical modeling approach”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1075-1088, 2003.
A. Amelio, “A new axiomatic methodology for the image similarity”, Applied Soft Computing, vol. 81, p. 105474, 2019
Z. Yu, and W. Wang, “Learning DALTS for cross-modal retrieval,” CAAI Transactions on Intelligence Technology, vol. 4, no. 1, pp. 9–16, 2019.
B. Zafar, R. Ashraf, N. Ali, and M. Ahmed., “Intelligent image classification-based on spatial weighted histograms of concentric circles”, Computer Science and Information Systems, vol. 15, no. 3, pp. 615–633, 2018.
S. Fadaei, R. Amirfattahi, and M. R. Ahmadzadeh, “Local derivative radial patterns: a new texture descriptor for content-based image retrieval”, Signal Processing, vol. 137, pp. 274–286, 2017.
A. Singla, M. Garg “CBIR Approach Based On Combined HSV, Auto Correlogram, Color Moments And Gabor Wavelet”, International Journal Of Engineering And Computer Science, ISSN:2319-7242, vol. 3, pp. 9007–9012, Oct, 2014.
M. Kuse, “Filter Gabor Library”, sumber: https://www.mathworks.com/matlabcentral/fileexchange/38844-gabor-image-features, 1 Februari 2021.
P. Pakutharivu, and M. V. Srinath, "Analysis of Fingerprint Image Enhancement Using Gabor Filtering with Different Orientation Field Values", Indonesian Journal of Electrical Engineering and Computer Science, vol. 5, pp. 427- 432, Feb, 2017.
D. T. Susetianingtias, H.S. Suryadi, S. Madenda, Rodiah, and Fitrianingsih, "Blood vessel extraction and bifurcations detection using hessian matrix of gaussian and euclidian distance", Journal of Theoretical and Applied Information Technology 95(15):3471-3478, Aug, 2017.
M. Kuse, V. Kalasannavar, N. Rajpoot, Y. Wang, and M. Khan, “Local isotropic phase symmetry measure for detection of beta cells and lymphocytes”, Journal of Pathology Informatics, 2(2), 2. https://doi.org/10.4103/2153-3539.92028, 2011.
