Journal of the Operations Research Society of China >

2020 , Vol. 8 >Issue 1: 117 - 132

DOI: https://doi.org/10.1007/s40305-018-0212-8

Latent Local Feature Extraction for Low-Resolution Virus Image Classification

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  • 1 Department of Mathematics, Shanghai University, Shanghai 200444, China;
    2 China Information Development Inc. Ltd., Shanghai 200444, China;
    3 School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China

Received date: 2018-03-20

  Revised date: 2018-06-07

  Online published: 2020-02-18

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Abstract

Virus image classification is a significant and challenging issue in both clinical virology and medical image processing. Due to the low-resolution virus images in the original dataset, there is tricky difficulty in extracting useful features from this kind of poor quality images adopting the traditional feature extraction methods. In this paper, we propose an effective and robust method, which eliminates the drawbacks of traditional local feature extraction methods and conducts latent local texture feature extraction thus to promote the accuracy of virus image classification. Firstly, the multi-scale principal component analysis (PCA) filters are learned from all original images. Then, it establishes a scale space for each PCA-filtered image by 2D Gaussian function. Finally, some typical feature descriptors are employed to extract texture features from all images, which include the original image and its filtered images by PCA and Gaussian filters. Aiming at the classification of low-resolution images, the proposed method solves the difficulty in extracting the essential feature from the original image and captures its latent and principal texture information from different perspectives in different filtered images. Experimental results show that the classification accuracy of the proposed method is much higher than state-of-the-art methods in the same low-resolution virus dataset, reaching 88.00%.

Key words： Low-resolution virus image; Principal component analysis; Scale space; CLBP; Support vector machine (SVM)

Cite this article

Zhi-Jie Wen, Zhi-Hu Liu, Yi-Chen Zong, Bao-Jun Li . Latent Local Feature Extraction for Low-Resolution Virus Image Classification[J]. Journal of the Operations Research Society of China, 2020 , 8(1) : 117 -132 . DOI: 10.1007/s40305-018-0212-8

References

[1] Ong, H.C.L.:Virus recognition in electron microscope images using higher order spectral features. NPhD thesis, Queensland University of Technology (2006)
[2] Goldsmith, C.S., Miller, S.E.:Modern uses of electron microscopy for detection of viruses. Clin. Microbiol. Rev. 22(4), 552-563(2009)
[3] Biel, S.S., Madeley, D.:Diagnostic virology-the need for electron microscopy:a discussion paper. J. Clin. Virol. 22(1), 1-9(2001)
[4] Zhang,Y.,Hung,T.,Song,J.,He,J.:Electron microscopy:essentials for viral structure,morphogenesis and rapid diagnosis. Sci. China Life Sci. 56(5), 421-430(2013)
[5] Nanni, L., Brahnam, S., Ghidoni, S., Menegatti, E.:Improving the descriptors extracted from the co-occurrence matrix using preprocessing approaches. Expert Syst. Appl. 42(22), 8989-9000(2015)
[6] Abdullah, M.F.A., Sayeed, M.S., Muthu, K.S., Bashier, H.K., Azman, A., Ibrahim, S.Z.:Face recognition with symmetric local graph structure (SLGS). Expert Syst. Appl. 42(14), 6131-6137(2014)
[7] Lowe, D.G.:Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91-110(2004)
[8] Yang, P., Yang, G.:Feature extraction using dual-tree complex wavelet transform and gray level co-occurrence matrix. Neurocomputing 197(C), 212-220(2016)
[9] Zhang, L., Tjondronegoro, D., Chandran, V.:Random Gabor based templates for facial expression recognition in images with facial occlusion. Neurocomputing 145(18), 451-464(2014)
[10] Chen,C.C.,Huang,C.L.:Markov random fields for texture classification.PatternRecogn.Lett. 14(11), 907-914(1993)
[11] Chan, T.H., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.:Multiple sparse representations classification. PLoS ONE 10(8), e0136827(2015)
[12] Ojala, T., Pietikainen, M., Maenpaa, T.:Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Eur. Conf. Comput. Vis. 24(7), 404-420(2000)
[13] Nanni, L., Lumini, A., Brahnam, S.:Survey on LBP based texture descriptors for image classification. Expert Syst. Appl. 39(3), 3634-3641(2012)
[14] Liao, S., Chung, A.C.S.:Survey on LBP based texture descriptors for image classification. In:Computer Vision-ACCV 2007:8th Asian Conference on Computer Vision, Tokyo, Japan, pp. 672-679(2007).
[15] Hussain, S.U., Triggs, B.:Visual recognition using local quantized patterns. Br. Mach. Vis. Conf. s3-2(94), 99.1-99.11(2012)
[16] Satpathy, A., Jiang, X., Eng, H.L.:LBP-based edge-texture features for object recognition. Br. Mach. Vis. Conf. 23(5), 1953-1964(2014)
[17] Zhang, W., Shan, S., Gao, W., Chen, X.:Local Gabor binary pattern histogram sequence (LGBPHS):a novel non-statistical model for face representation and recognition. Tenth IEEE Int. Conf. Comput. Vis. 1(1), 786-791(2005)
[18] Yang, J., Jiao, Y., Xiong, N., Park, D.S.:Fast face gender recognition by using local ternary pattern and extreme learning machine. KSII Trans. Internet Inf. Syst. 7(7), 1705-1720(2013)
[19] dos Santos, F.L.C., Paci, M., Nanni, L., Brahnam, S., Hyttinen, J.:Computer vision for virus image classification. Biosyst. Eng. 138, 11-12(2015)
[20] Nanni, L., Brahnam, S., Lumini, A.:Combining different local binary pattern variants to boost performance. Expert Syst. Appl. 38(5), 6209-6216(2011)
[21] Guo, Z., Zhang, L., Zhang, D.:A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657-1663(2010)
[22] Guo, Z., Wang, X., Zhou, J., You, J.:Robust texture image representation by scale selective local binary patterns. IEEE Trans. Image Process. 25(2), 687-699(2016)
[23] Li, Z., Liu, G., Yang, Y., You, J.:Scale- and rotation-invariant local binary pattern using scale-adaptive texton and subuniform-based circular shift. IEEE Trans. Image Process. 21(4), 2130-2140(2012)
[24] Song, K., Yan, Y., Zhao, Y., Liu, C.:Adjacent evaluation of local binary pattern for texture classification. J. Vis. Commun. Image Represent. 33(C), 323-339(2015)
[25] Ahonen, T., Matas, J., He, C., Pietikäinen, M.:Rotation invariant image description with local binary pattern histogram Fourier features. Image Anal. 5575(4), 61-70(2009)
[26] Ying, S., Wen, Z., Shi, J., Peng, Y., Peng, J., Qiao, H.:Manifold preserving:an intrinsic approach for semisupervised distance metric learning. IEEE Trans. Neural Netw. Learn. Syst. 29(7), 2731-2742(2018)
[27] Hu, L., Hu, J., Ye, Z., Shen, C., Peng, Y.:Performance analysis for SVM combining with metric learning. Neural Process Lett (2018). https://doi.org/10.1007/s11063-017-9771-7
[28] Ren, J., Jiang, X., Yuan, J.:Relaxed local ternary pattern for face recognition. In:IEEE International Conference on Image Processing, pp. 3680-3684(2014)
[29] Chang, C.C., Lin, C.J.:LIBSVM:a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 389-396(2007)
[30] Nanni, L., Paci, M., Brahnam, S., Ghidoni, S., Menegatti, E.:Virus image classification using different texture descriptors. In:The International Conference on Bioinformatics and Computational Biology. 56-61(2013).
[31] Kylberg, G., Uppstrom, M., Sintorn, I.M.:Virus texture analysis using local binary patterns and radial density profiles. Secur. Commun. Netw. 7042, 2153-2159(2011)
[32] Wen, Z., Li, Z., Peng, Y., Ying, S.:Virus image classification using multi-scale completed local binary pattern features extracted from filtered images by multi-scale principal component analysis. Pattern Recogn. Lett. 79, 25-30(2016)
[33] Chan, T.H., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.:PCANet:A simple deep learning baseline for image classification? IEEE Trans. Image Process. 24(12), 5017-5032(2015)
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