بازیابی تصاویر بر اساس محتوا با استفاده از ترکیب روشهای PCA و LDA
محورهای موضوعی : مهندسی الکترونیک
فاطمه معمار
1
(گروه کامپیوتر، دانشکده فنی و مهندسی، واحد شاهرود، دانشگاه آزاد اسلامی، شاهرود، ایران)
محمدمهدی حسینی
2
(گروه کامپیوتر، دانشکده فنی و مهندسی، واحد شاهرود، دانشگاه آزاد اسلامی، شاهرود،ایران)
علیرضا جلالی
3
(گروه کامپیوتر، دانشکده فنی و مهندسی، واحد شاهرود، دانشگاه آزاد اسلامی، شاهرود، ایران)
کلید واژه: Principal Component Analysis (PCA), Linear discriminant analysis (LDA), بازیابی تصاویر براساس محتوا, ویژگی‌های تصاویر, Content-Based Image Retrieval on, Features Image, آنالیز اجزای اصلی (PCA), آنالیز تفکیک پذیری خطی (LDA),
چکیده مقاله :
امروزه، تصاویر دیجیتال کاربرد گسترده‌ای در تشخیص بیماری‌ها، چهره، اثر انگشت، امنیت سیستم‌ها و حوزه‌های دیگر پیدا کرده‌اند. از این رو، ارائه الگوریتمی دقیق در جستجو و بازیابی تصاویر از اهمیت بالایی برخوردار است. در این مقاله با استفاده از ترکیب روشهایPCA وLDA برای بازیابی تصاویر ارائه شده است. در این روش ابتدا تصاویر رنگی موجود در فضای RGB به فضای HSV منتقل، سپس ویژگی‌های رنگ، شکل و بافت از مولفه “V” فضای رنگ HSV استخراج می‌شوند. در ادامه بردار ویژگی پیشنهادی با استفاده از هیستوگرامLDP ، هیستوگرام رنگ، هیستوگرام Tamura و ماتریس رخداد مشترک ساخته می‌شود. در ادامه با ترکیب دو روش PCA وLDA کاهش ویژگی انجام و در نهایت طبقه‌بندی صورت می‌پذیرد. برای بررسی روش پیشنهادی چهار سناریو طراحی و ارزیابی صورت پذیرفت. با توجه به آزمایشات صورت گرفته و ارزیابی انجام شده، دقت بدست آمده 97.6 حاصل گردید که حاکی از عملکرد مناسب روش پیشنهادی در مقایسه با کارهای مشابه می‌باشد.
Nowadays, digital images are widely used in the diagnosis of disease, facial and fingerprint, security systems, and more. Therefore, providing an accurate algorithm in image recognition and retrieval is very important. This paper presents a combination of PCA and LDA methods for image retrieval. In this method, first, the color images in the RGB space are transferred to the HSV space, then the color, shape, and texture properties are extracted from the "V" component of the HSV color space. The proposed feature vector is then constructed using the LDP histogram, color histogram, Tamura histogram, and common event matrix. Then, by combining the two methods of PCA and LDA, the specificity is reduced and finally, the classification is done. Four scenarios were designed and evaluated to evaluate the proposed method. According to experimental result and evaluation criteria, The accuracy obtained was 97.6 which indicates the proper performance of the proposed method compared to similar tasks.
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_||_[1] G.H. Liu and J.Y. Yang, “Content-based image retrieval using color difference histogram”, Pattern recognition, vol. 46, no. 1, pp.188-198, 2013.
[2] Z. Xia, X. Wang, L. Zhang, Z. Qin, X. Sun, and K. Ren, “A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing”, IEEE transactions on information forensics and security, vol.11, no.11, pp.2594-2608, 2016.
[3] S. Banuchitra, and K. Kungumaraj, “A comprehensive survey of content based image retrieval techniques”, International Journal of Engineering and Computer Science, vol. 5, no.8, 2016.
[4] D. Srivastava, B. Rajitha, S. Agarwal, and S. Singh, “Pattern-based image retrieval using GLCM.” Neural Computing and Applications, vol.32,no.8, pp.1-14, 2018.
[5] W. Zhou, H. Li, and Q. Tian, “Recent advance in content-based image retrieval: A literature survey”, ArXiv preprint arXiv:1706.06064, 2017.
[6] R. Ashraf, M. Ahmed, S. Jabbar, S. Khalid, A. Ahmad, S. Din, and G. Jeon, “Content based image retrieval by using color descriptor and discrete wavelet transform”, Journal of medical systems, vol.42, no.3, p.44, 2018.
[7] S. Soman, M. Ghorpade, V. Sonone, and S. Chavan., “Content based image retrieval using advanced color and texture features”, In International Conference in Computational Intelligence (ICCIA), Vol. 3, No. 4, pp. 1-5, 2012.
[8] M. Singha and K. Hemachandran, Content based image retrieval using color and texture, Signal & Image Processing: An International Journal (SIPIJ), vol. 3, pp. 39-57, 2012.
[9] J. Zhou, T. Xu, and W. GAO, “Content based image retrieval using local directional pattern and color histogram”, In Optimization and Control Techniques and Applications, pp. 197-211, 2014.
[10] Y. Xu, X. Zhao, and J. Gong, “A Large-Scale Secure Image Retrieval Method in Cloud Environment”. IEEE Access, vol.7, pp.160082-160090,2019.
[11] P. Liu, J.M. Guo, C.Y. Wu, and D. Cai, “Fusion of deep learning and compressed domain features for content-based image retrieval”, IEEE Transactions on Image Processing, vol.26, no.12, pp.5706-5717, 2017.
[12] 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.
[13] G.H. Liu, J.Y. Yang, and Z. Li, “Content-based image retrieval using computational visual attention model”. pattern recognition, vol, 48, no.8, pp.2554-2566,2015.
[14] A. Alzu'bi, A. Amira, and N. Ramzan, “Content-based image retrieval with compact deep convolutional features”, Neurocomputing, vol.249, pp.95-105, 2017.
[15] A. Yalavarthi, , K. Veeraswamy and K.A. Sheela,, July. “Content based image retrieval using enhanced Gabor wavelet transform”, International Conference on Computer, Communications and Electronics, 2017,pp. 339-343.
[16] G.L. Shen, and X.J. Wu, “Content based image retrieval by combining color, texture and CENTRIST”, Constantinides International Workshop on Signal Processing (CIWSP), 2013, pp. 1-4,.
[17] M. M. Rahman, P. Bhattacharya, and B. C. Desai, “A unified image retrieval framework on local visual and semantic concept-based feature spaces”, Journal of Visual Communication and Image Representation, vol. 20, pp. 450-462, 2009.
[18] L. Xavier, B. Thusnavis, and D. Newton, “Content based image retrieval using textural features based on pyramid-structure wavelet transform”, in Electronics Computer Technology (ICECT) Conf., 2011, pp. 79-83.
[19] S.-H. Y. a. W. P. Sung-Kwan Oh, “Design of face recognition algorithm using PCA -LDA combined for hybrid data pre-processing and polynomial-based RBF neural networks: Design and its application”, Expert Systems with Applications, vol. 40, pp. 1451–1466, 2013.
[20] N. Sebe, and M. S. Lew, “Texture Features for Content-Based Image Retrieval”, Principles of Visual Information Retrieval, pp.51-86, 2011.
[21] A. K. Yadav, R. Roy, and V. A. P. Kumar, “Survey on Content-based Image Retrieval and Texture Analysis with Applications”, International Journal of Signal Processing, Image Processing and Pattern Recognition, vol.7, no. 6, pp. 41-50, 2014.
[22] C. Seung-Seok, S.H. Cha, and C. C. Tappert, “A survey of binary similarity and distance measures”, Journal of Systemics, Cybernetics and Informatics, vol.8, no. 1, pp. 43-48, 2010.
[23] S. Soman, M. Ghorpade, V. Sonone, and S. Chavan, “Content based image retrieval using advanced color and texture features”, in International Conference in Computational Intelligence (ICCIA), 2012.
[24] K. Arthi, J. Vijayaraghavan , “Content based image retrieval algorithm using colour models”, international journal of advanced research in computer and communication engineering.,vol2.no.3, pp. 1343-1347, 2013.
[25] I. F. Rajam,, and S. Valli, “Content-Based Image Retrieval Using a Quick KNN-Binary Decision Tree–QKNNBDT”, In International Conference on Digital Image Processing and Information Technology,2011, pp. 11-22.
[26] I. F. Rajam, and S. Valli. “SRBIR: semantic region based image retrieval by extracting the dominant region and semantic learning”. In J. Comput. Sci,vol.7,no.3,pp.400-408, 2011.
[27] H. C. Akakin, and N. G. Metin, “Content-based microscopic image retrieval system for multi-image querie”, IEEE transactions on information technology in biomedicine ,vol.16, no. 4, pp.758-769,2012.