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An improved MIMLRBF natural scene image classification based on spectral clustering

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DOI: 10.23977/jipta.2016.11006 | Downloads: 50 | Views: 3096

Author(s)

Shanshan Zhang 1, Wei WU 1

Affiliation(s)

1 School of Information Engineering, Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Wuhan University of Technology, Wuhan, Hubei 430070, China

Corresponding Author

Shanshan Zhang

ABSTRACT

Natural scene image classification problems can be showed by multi-instances multi-labels learning model (MIML), and MIMLRBF algorithm achieved good effect. MIMLRBF algorithm is based on the clustering technology and neural network for classification. Related experiments show that the measure of the package and the selection of the cluster center have an important impact on the result of image classifications, in order to obtain better clustering accuracy, first of all, this article introduced the spectral clustering method in the training process, which can make the sample package center more reasonable; Second, we redefined the distance between the sample packages, to overcome effectively the influence of the isolated examples on the distance to the sample packages. The experimental results show that the proposed approach can effectively improve the classification accuracy, and it is better than MIMLRBF algorithm on the various performance.

KEYWORDS

image classification; Hausdorff distance; spectral clustering; MIMLRBF algorithm

CITE THIS PAPER

Wei, W. and Shanshan Z. (2016) An improved MIMLRBF natural scene image classification based on spectral clustering. Journal of Image Processing Theory and Applications (2016) 1: 27-31.

REFERENCES

[1] Zhou Z-H, Zhang M-L. Multi-instance multi-label learning with application to scene classification[C]. Advances in neural information processing systems. 2006: 1609–1616.
[2] Zhang M-L, Wang Z-J. MIMLRBF: RBF neural networks for multi-instance multi-label learning[J]. Neurocomputing, Elsevier, 2009, 72(16): 3951–3956.
[3] Zhang M-L. A k-nearest neighbor based multi-instance multi-label learning algorithm[C]. Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on. 2010: 207–212.
[4] Shi J,Malik J. Normalized cuts and image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):888-905.
[5] Ng A-Y, Jordan M-I,Weiss Y. On spectral clustering:Analysis and an algorithm[C]. Proceedings of the 14th Advances in Neural Information Processing Systems.Cambridge,MA:MIT Press,2002:849-856.
[6] Wang J, Zucker J-D. Solving multiple-instance problem: A lazy learning approach[J]. Morgan Kaufmann, 2000:1119-1125.
[7] Zhang M-L, Zhou Z-H. Multi-instance clustering with applications to multi-instance prediction[J]. Applied Intelligence, Springer, 2009, 31(1): 47–68.
[8] Wu J, Huang S, Zhou Z. Genome-wide protein function prediction through multi-instance multi-label learning[J]. Computational Biology and Bioinformatics, IEEE/ACM Transactions on, IEEE, 2014,11(5):891-902.

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