Abstract
There have been two problems in the implementation of a content-based image retrieval (CBIR) system in web. One is the absence of a standardized way to describe image content, the other is the disregard for the special needs of individual users To address these two problems, in this paper, a human-oriented CBIR system is presented which is implemented by applying MPEG-7 descriptors. In the new system, a multi-feature space is established and both homogeneous texture descriptor and color layout descriptor are used. Since there are difference in human perceptions of color and texture, in order to successfully retrieve an image which caters to the users, PGA (parallel genetic algorithm) is employed to adjust the weight of each feature space. The experimental evidence shows that the system is robust in general format by using MPEG-7 and it is capable of matching the user profile as well.
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References
Xiang-yang, L., Yue-ting, Z., Yun-he, P.: Technique and Systems of Content-based Image Retrieval. Journal of Computer Research Development 38(3), 344–354 (2001)
Müller, H., Müller, W., Squire, D.M., Marchand-Maillet, S., Pun, T.: Performance evaluation in content-based image retrieval: Overview and proposals. Pattern Recognition Letters 22(5), 593–601 (2001)
Multimedia Content Description Interface - Part 3: Visual, Final Draft for International Standard, ISO/IEC/JTC1/SC29/WG11, Doc. N4358 (2001)
Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications (Numerical Insights). CRC Press Inc., Boca Raton (2009)
Man, K.F., Tang, K.S., Kwong, S.: Genetic Algorithms: Concepts and Applications. IEEE Transaction on Industrial Electronics 43(5), 519–534 (1996)
Seo, K.-K.: Content-Based Image Retrieval by Combining Genetic Algorithm and Support Vector Machine. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007, Part II. LNCS, vol. 4669, pp. 537–545. Springer, Heidelberg (2007)
Zeng, X.-p., Cheng, Y.-f., Li, Y.-m.: Real Adaptive genetic algorithm with parallel operators. Application Research of Computers 25(6), 1687–1689 (2006)
Ma, h., Sun, W., Dai, J.-s., Wen, b.-c.: Bearing parameter optimization of a large-scale centrifugal compressor based on orthogonal experiment. Computer Integrated Manufacturing Systems 16(2), 390–395 (2010)
Zhou, X.-g., Li, W.-m., Liu, Y.: Modeling of UAV Search Strategy Based on Nearly Orthogonal Latin Hypercube Experiment. Computer Engineering 36(9), 1–3 (2010)
Squire, D.M., Müller, H., Müller, W., Marchand-Maillet, S., Pun, T.: Design and Evaluation of a Content-based Image Retrieval System. In: Design & Management of Multimedia Information Systems: Opportunities & Challenges, pp. 125–151. IGI Publishing, Hershey (2001)
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Liu, M., Li, J., Liu, H. (2010). Human-Oriented Image Retrieval of Optimized Multi-feature via Genetic Algorithm. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds) Information Computing and Applications. ICICA 2010. Lecture Notes in Computer Science, vol 6377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16167-4_44
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DOI: https://doi.org/10.1007/978-3-642-16167-4_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16166-7
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