Abstract
This paper presents a series of new image descriptors based on statistical thermodynamics and discusses their application in content-based image retrieval and image clustering. The paper puts forward image descriptors which represent macro-visual characteristics such as “image energy,” “image pressure,” “image mass,” and “image temperature” according to the analysis-localized sub-system within the statistical thermodynamic theory. We can find a lot of mathematical laws by applying statistical thermodynamic theory in digital image processing. The proposed method has the characteristics of the fast calculation. Experiment verifies the rationality and effectiveness of the proposed method.
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Castells, M.: The power of identity: the information age: economy, society, and culture. Wiley-Blackwell, Hoboken (2011)
Joia, P.: Class-specific metrics for multidimensional data projection applied to CBIR. Vis. Comput. 28(10), 1027–1037 (2012)
Hirata, K., Kato, T.: Query by visual example—content based image retrieval. In: Proceedings of the 3rd International Conference on Extending Database Technology: Advances in Database, Vienna, Austria 1992
Kekre, H.B.: Sectorization of DCT-DST plane for column wise transformed color images in CBIR. Technol. Sys. Manage. Commun. Comput. Inf. Sci. 145, 55–60 (2011)
Qi, H., Li, K., Shen, Y., et al.: An effective solution for trademark image retrieval by combining shape description and feature matching. Pattern Recogn. 43(6), 2017–2027 (2010)
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. Image Proc. IEEE Trans. 19(6), 1635–1650 (2010)
Morel, J.M., Yu, G.: ASIFT: a new framework for fully affine invariant image comparison. SIAM J. Imaging Sci. 2(2), 438–469 (2009)
Sandler, S.I.: An introduction to applied statistical thermodynamics. Wiley, London (2010)
Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vision Graph. Image Proc. 29(3), 273–285 (1985)
Yang, Y., Xu, D., Nie, F., et al.: Image clustering using local discriminant models and global integration. Image Proc. IEEE Trans. 19(10), 2761–2773 (2010)
Zhao, Z.L., Liu, B., Li, W.: Image clustering based on extreme K-means algorithm. IEIT J. Adapt. Dyn. Comput. 2012(1), 12–16 (2012)
Bian, W., Tao, D.: Biased discriminant Euclidean embedding for content-based image retrieval. Image Proc. IEEE Trans. 19(2), 545–554 (2010)
Gomez R.: Integrating technology in a statistics course for a special program at Florida International University. (2013)
Acknowledgments
Project supported by the National Nature Science Foundation of China (No.61073121), The National Key Technology R&D Program (No.2013BAK07B04), Natural Science Foundation of Hebei Province of China (No. F2013201170), and Medical Engineering Alternate Research Center Open Foundation of Hebei University (No. BM201102).
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© 2014 Springer India
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Li, K., Luo, S., Meng, Q., Gao, Y., Li, H. (2014). Image Descriptors Based on Statistical Thermodynamics and Applications. In: Patnaik, S., Li, X. (eds) Proceedings of International Conference on Computer Science and Information Technology. Advances in Intelligent Systems and Computing, vol 255. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1759-6_3
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DOI: https://doi.org/10.1007/978-81-322-1759-6_3
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1758-9
Online ISBN: 978-81-322-1759-6
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