Abstract
Image classification is a core task in many applications of computer vision. Recognition of weather conditions based on large-volume image datasets is a challenging problem. However, there has been little research on weather-related recognition using color images, particularly with large datasets. In this study, we proposed a metric learning framework to investigate a two-class weather classification problem. We improve the classification accuracy using metric learning approaches. Extracting features from images is a challenging task and practical requirements such as domain knowledge and human intervention. In this paper, we define several categories of weather feature cures based on observations of outdoor images captured under different weather conditions. Experimental results show that a classifier based on metric learning framework is effective in weather classification and outperforms the previous approach when using the same dataset.
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References
Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13:21–27
Davis J, Kulis B, Jain P, Sra S, Dhillon I (2007) Information-theoretic metric learning. Proc. Int’l Conf. Machine Learning pp. 209–216
Elhoseiny M, Huang S, Elgammal E (2015) Weather classification with deep convolutional neural networks. IEEE International Conference on Image Processing (ICIP), pp. 3349–3353
Lu C, Shi J, Jia J (2013) Online robust dictionary learning. Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 415–422
Lu C, Lin D, Jia J, Tang CK (2014) Two-class weather classification. Proc. IEEE Conf. Computer Vision and Pattern Recognition pp. 3718–3725
Rafael A, Gonzalez C, Woods RE (2002) Digital image processing 2nd edition. Prentice Hall, Upper Saddle River
Roser M, Moosmann F (2008) Classification of weather situations on single color images. Proc. of the IEEE Intelligent Vehicles Symposium, pp. 798–803
Russell BC, Torralba A, Murphy KP, Freeman WT (2008) Labelme: A Database and Web-Based Tool for Image Annotation. Int J Comput Vis 77:157–173
Vapnik VN (1998) The nature of statistical learning theory. Wiley, New York
Wang J, Yang J, Yu K, Lv F, Huang T, Gong Y (2010) Locality constrained linear coding for image classification. Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 3360–3367
Weinberger KG, Saul LK (2009) Distance Metric Learning for Large Margin Nearest Neighbor Classification. J Mach Learn Res 10:207–244
Xiao J, Hays J, Ehinger K, Oliva A, Torralba A (2010) SUN database: large-scale scene recognition from Abbey to Zoo. Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 3485–3492
Yan X, Luo Y, Zheng X (2009) Weather recognition based on images captured by vision system in vehicle. Proc. of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III 5553: 390–398
Zhang Z, Ma H, Fu H, Wang X (2015) Outdoor air quality inference from single image. Proc. of International Conference on MultiMedia Modeling, pp. 13–25
Zheng WS, Gong S, Xiang T (2012) Reidentification by Relative Distance Comparison. IEEE Trans Pattern Anal Mach Intell 35(3):653–668
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Lin, FJ., Wang, TP. Metric learning for weather image classification. Multimed Tools Appl 77, 13309–13321 (2018). https://doi.org/10.1007/s11042-017-4948-7
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DOI: https://doi.org/10.1007/s11042-017-4948-7