Abstract
Nowadays, multimedia data, especially image, are of increasing number. Content-based image retrieval system is becoming an important tool to assist user in managing his/her collection of images. This work presents a development of an image search system for a particular image database containing various types of images. We present a study of visual descriptors for this database and a simple strategy to speed-up the retrieval process. We also present a relevance feedback technique based on semi-supervised learning technique. Experimental result of the proposed system seems promising.
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Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., Yanker, P.: Query by image and video content: The qbic system. IEEE Computer 28(9), 23–32 (1995)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary pattern. IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)
Pass, G., Zabih, R., Miller, J.: Comparing images using color coherence vectors. In: Proceedings of the ACM Multimedia (1996)
Smith, J.R., Chang, S.F.: Query by color regions using visualseek content-based visual query system. In: Proceedings of the ACM Multimedia (1996)
Vasconcelos, N.: From pixels to semantic spaces: Advances in content-based image retrieval. IEEE Computer, 20–26 (2008)
Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advance in Neural Information Processing Systems, NIPS (2008)
Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using gaussian fields and harmonic functions. In: Proceedings of the International Conference on Machine Learning, ICML (2003)
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© 2011 Springer-Verlag Berlin Heidelberg
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Marukatat, S. (2011). Development of a Search System for Heterogeneous Image Database. In: Theeramunkong, T., Kunifuji, S., Sornlertlamvanich, V., Nattee, C. (eds) Knowledge, Information, and Creativity Support Systems. Lecture Notes in Computer Science(), vol 6746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24788-0_12
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DOI: https://doi.org/10.1007/978-3-642-24788-0_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24787-3
Online ISBN: 978-3-642-24788-0
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