Color and Texture Feature for Remote Sensing – Image Retrieval System: A Comparative Study
In this study, we proposed score fusion technique to improve the performance of remote sensing image retrieval system (RS-IRS) using combination of several features. The representation of each feature is selected based on their performance when used as single feature in RS-IRS. Those features are color moment using L*a*b* color space, edge direction histogram extracted from Saturation channel, GLCM and Gabor Wavelet represented using standard deviation, and local binary pattern using 8-neighborhood. The score fusion is performed by computing the value of image similarity between an image in the database and query, where the image similarity value is sum of all features similarity, where each of feature similarity has been divided by SVD value of feature similarity between all images in the database and query from related feature. The feature similarity is measured by histogram intersection for local binary pattern, whereas the color moment, edge direction histogram, GLCM, and Gabor are measured by Euclidean Distance. The final result shows that the best performance of remote sensing image retrieval in this study is a system which uses the combination of color and texture features (i.e. color moment, edge direction histogram, GLCM, Gabor wavelet, and local binary pattern) and uses score fusion in measuring the image similarity between query and images in the database. This system outperforms the other five individual feature with average precision rates 3%, 20%, 13%, 11%, and 9%, respectively, for color moment, edge direction histogram, GLCM, Gabor wavelet, and LBP. Moreover, this system also increase 17% compared to system without score fusion, simple-sum technique.
Keywords: Color Moment, Edge Direction Histogram, GLCM, Gabor Wavelet, Local Binary Pattern, Score Fusion
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ABOUT THE AUTHORS
Retno Kusumaningrum
Retno Kusumaningrum achieved her undergraduate degree in Department of Mathematics from Diponegoro University, Semarang, Indonesia, where she is currently working toward as lecturer in Department of Informatics, in 2003. She earned her master degree in Faculty of Computer Science from University of Indonesia, Depok, Indonesia, in 2010. Currently, she is studying for her doctoral degree in Faculty of Computer Science, University of Indonesia, Jakarta, Indonesia. Her current research activities are in spatial pattern and image retrieval system, particularly feature extraction, relevance feedback, and objective evaluation, with main application in remote sensing domains.
Aniati Murni Arymurthy
Aniati Murni Arymurthy is a Professor in Faculty of Computer Science, University of Indonesia. She graduated from Department of Electrical Engineering, University of Indonesia, Jakarta, Indonesia. She earned her Master of Science in Department of Computer and Information Sciences, The Ohio State University (OSU), Columbus, Ohio, USA. She also holds Doktor from Department of Opto-Electronics and Laser Applications, University of Indonesia, Jakarta, Indonesia and a sandwich program at the Laboratory for Pattern Recognition and Image Processing (PRIP Lab), Department of Computer Science, Michigan State University (MSU), East Lansing, Michigan, USA. Her main research activities are image processing and pattern recognition.
Retno Kusumaningrum
Retno Kusumaningrum achieved her undergraduate degree in Department of Mathematics from Diponegoro University, Semarang, Indonesia, where she is currently working toward as lecturer in Department of Informatics, in 2003. She earned her master degree in Faculty of Computer Science from University of Indonesia, Depok, Indonesia, in 2010. Currently, she is studying for her doctoral degree in Faculty of Computer Science, University of Indonesia, Jakarta, Indonesia. Her current research activities are in spatial pattern and image retrieval system, particularly feature extraction, relevance feedback, and objective evaluation, with main application in remote sensing domains.
Aniati Murni Arymurthy
Aniati Murni Arymurthy is a Professor in Faculty of Computer Science, University of Indonesia. She graduated from Department of Electrical Engineering, University of Indonesia, Jakarta, Indonesia. She earned her Master of Science in Department of Computer and Information Sciences, The Ohio State University (OSU), Columbus, Ohio, USA. She also holds Doktor from Department of Opto-Electronics and Laser Applications, University of Indonesia, Jakarta, Indonesia and a sandwich program at the Laboratory for Pattern Recognition and Image Processing (PRIP Lab), Department of Computer Science, Michigan State University (MSU), East Lansing, Michigan, USA. Her main research activities are image processing and pattern recognition.
