Rotation-invariant texture image retrieval using particle swarm optimization and support vector regression |
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| Affiliation: | 1. Department of Information Management, National Formosa University, Hu-Wei, Yun-Lin 632, Taiwan, ROC;2. Department of Information Management, Chienkuo Technology University, Chang-Hua, Chang-Hua 500, Taiwan, ROC;1. Department of Energy Systems Engineering, Graduate School of Natural and Applied Sciences, Süleyman Demirel University, 32260 Isparta, Turkey;2. Department of Mechatronics Engineering, Faculty of Technology, Isparta University of Applied Sciences, 32260 Isparta, Turkey;3. Department of Energy Systems Engineering, Faculty of Technology, Muğla Sıtkı Koçman University, 48000 Muğla, Turkey;1. School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, Shandong 250014, China;2. School of Economics and Management, Civil Aviation University of China, Tianjin 300300, China;1. Department of Information Engineering, Electronics, and Telecommunications, SAPIENZA University of Rome, Via Eudossiana 18, 00184 Rome, Italy;2. Department of Computer Science, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada;1. Department of Control, Automation, and System Analysis, Saint Petersburg State Forest Technical University, Institutsky pereulok 5, Saint-Petersburg 194021, Russia;2. Department of Mathematics, University of York, York YO10 5DD, United Kingdom;1. Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, Madrid 28871, Spain;2. Optima Unit, Tecnalia Research & Innovation, Zamudio 48170, Bizkaia, Spain;3. Department of Energy Resource, Iberdrola, Madrid, Spain;4. Department of Energy IT, Gachon University, Seongnam 461-701, South Korea |
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| Abstract: | This paper presents a novel rotation-invariant texture image retrieval using particle swarm optimization (PSO) and support vector regression (SVR), which is called the RTIRPS method. It respectively employs log-polar mapping (LPM) combined with fast Fourier transformation (FFT), Gabor filter, and Zernike moment to extract three kinds of rotation-invariant features from gray-level images. Subsequently, the PSO algorithm is utilized to optimize the RTIRPS method. Experimental results demonstrate that the RTIRPS method can achieve satisfying results and outperform the existing well-known rotation-invariant image retrieval methods under considerations here. Also, in order to reduce calculation complexity for image feature matching, the RTIRPS method employs the SVR to construct an efficient scheme for the image retrieval. |
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| Keywords: | Content-based image retrieval Log-polar mapping Fast Fourier transform Zernike moment Particle swarm optimization Support vector regression |
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