A Study of Spore Image Classification Based on Feature Extraction

Abstract:

Fungal diseases are the major diseases of agricultural production, and have brought tremendous impact to it. Identification of spore morphology plays an important role in the identification of fungi. This paper uses the microscopy images of two kinds of fungal spore and utilizes the technology of image analysis and recognition to classify them. We firstly get the underlying feature descriptors of these two kinds of microscopy images by RGB SIFT (Scale Invariant Feature Transform), then create the visual word dictionary using K-means clustering algorithm, at finally we use LDA, KNN and SVM to classify these two kinds of images. The results indicate that the classification of spore image based on feature extraction is feasible. In our future work, we will conduct the classification of related species and highly similar spore images.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 4774-4778
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.4774
• Online since: May 2014
• Authors: Lu Lu Yue*, Ming Yang, Li Peng
• Keywords: K-Means Clustering Algorithm, K-Nearest Neighbor (KNN) Algorithm, Latent Dirichlet Allocation, RGB SIFT (Scale Invariant Feature Transform), Support Vector Machine (SVM)

References

[1] Deng Jizhong, Li Min, Yuan Zhibao, et al. Feature extraction and classification of Tilletia diseases based on image recognition[J]. Transactions of the CSAE, 2012, 28(3): 176-176.

[3] David G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision 60(2), 91-110, (2004).

DOI: 10.1023/b:visi.0000029664.99615.94

[4] Lingbo, Mingyuan Zhou , Guillermo Sapiroy , Lawrence Carin. On the Integration of Topic Modeling and Dictionary Learning[C]. Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA, USA, (2011).

[5] Chris Ding and Xiaofeng He, K-means Clustering via Principal Component Analysis, Proceeding of International Conference Machine Learning, July 2004, pp.225-232.

[6] Blei, D, A. Ng, and M. Jordan, Latent Dirichlet allocation[J], Journal of Machine Learning Research, 993-1022, (2003).

[7] Jonathan Chang and David M. Blei. Relational Topic Models for Document Networks[C], In Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS), (2009).

[8] K. Barnard, P. Duygulu, D. Forsyth, N. de Freitas, D. M. Blei, and M. I. Jordan, Matching Words and Pictures, J. Mach. Learn. Res (2003) 1107-1135.

[9] J. Tang and P. H. Lewis, A study of quality issues for image auto-annotation with the Corel data-set[C], IEEE Transactions on Circuit, (2007).

[10] Li Teng, Mei Tao, Kweon In-So, Hua Xian-Sheng, Contextual bag-of-words for visual categorization[C], IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, pp.381-392, (2011).

DOI: 10.1109/tcsvt.2010.2041828

[11] Blei, D, Probabilistic Models of Text and Images, Division of Computer Science (2004).

[12] Cover, T., Hart, P. , Nearest-neighbor pattern classification, Information Theory, IEEE Transactions on, Jan. 1967, pp.21-27.

DOI: 10.1109/tit.1967.1053964

[13] Zhang L, Lin F, Zhang B. Support vector machine learning for image retrieval[C], Image Processing, 2001. Proceedings. 2001 International Conference on. IEEE, 2001, 2: 721-724.

DOI: 10.1109/icip.2001.958595

[14] Cusano C, Ciocca G, Schettini R. Image annotation using SVM[C]/Electronic Imaging 2004. International Society for Optics and Photonics, 2003: 330-338.