Abstract
To efficiently and accurately classify a large image collection, this paper proposes a novel interactive system by incorporating active learning, online learning and user intervention. Given an image collection, our system iteratively alternates the interactive annotation and verification until all the images are classified. The main advantage is that it provides faster interactive classification rates than alternative approaches. Our system achieves this goal by a unified active learning algorithm that selects the images to be annotated or verified, which requires a probability model for simulating the time cost of human input during manual intervention. To assist manual annotation and verification, we generate the classification hypothesis of the selected images using a conditional random field (CRF) framework, which combines the cues from an online learned classifier and pairwise similarities of unlabeled images. Experimental results demonstrated the effectiveness of the method.
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References
Bodesheim, P., Freytag, A., Rodner, E., Kemmler, M., Denzler, J.: Kernel null space methods for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3374–3381 (2013)
Dosovitskiy, A., Fischer, P., Springenberg, J.T., Riedmiller, M., Brox, T.: Discriminative unsupervised feature learning with exemplar convolutional neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(9), 1734–1747 (2016)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2007 (VOC 2007) Results. http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106(1), 59–70 (2007)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778 (2016)
Hu, J., Sun, Z., Li, B., Wang, S.: PicMarker: data-driven image categorization based on iterative clustering. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10114, pp. 172–187. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54190-7_11
Hu, J., Sun, Z., Li, B., Yang, K., Li, D.: Online user modeling for interactive streaming image classification. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B.Þ., Satoh, S. (eds.) MMM 2017. LNCS, vol. 10133, pp. 293–305. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51814-5_25
Joshi, A.J., Porikli, F., Papanikolopoulos, N.: Multi-class active learning for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 2372–2379. IEEE (2009)
Kolmogorov, V.: Convergent tree-reweighted message passing for energy minimization. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1568–1583 (2006)
Krishnakumar, A.: Active learning literature survey. Technical reports, University of California, Santa Cruz. 42 (2007)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178. IEEE (2006)
Lee, D.H.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML, vol. 3, p. 2 (2013)
Li, L.J., Fei-Fei, L.: What, where and who? Classifying events by scene and object recognition. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8. IEEE (2007)
Li, X., Guo, Y.: Adaptive active learning for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 859–866 (2013)
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Vinokurov, N., Weinshall, D.: Novelty detection in multiclass scenarios with incomplete set of class labels. arXiv preprint arXiv:1604.06242 (2016)
Wang, J., Zhao, P., Hoi, S.C.H.: Exact soft confidence-weighted learning. In: ICML (2012)
Wang, K., Zhang, D., Li, Y., Zhang, R., Lin, L.: Cost-effective active learning for deep image classification. IEEE Trans. Circuits Syst. Video Technol. (2016)
Xu, Z., Yu, K., Tresp, V., Xu, X., Wang, J.: Representative sampling for text classification using support vector machines. In: Sebastiani, F. (ed.) ECIR 2003. LNCS, vol. 2633, pp. 393–407. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36618-0_28
Yi, L., Kim, V.G., Ceylan, D., Shen, I., Yan, M., Su, H., Lu, A., Huang, Q., Sheffer, A., Guibas, L., et al.: A scalable active framework for region annotation in 3D shape collections. ACM Trans. Graph. (TOG) 35(6), 210 (2016)
Acknowledgment
This work is supported by the National High Technology Research and Development Program of China (2007AA01Z334), National Natural Science Foundation of China (61321491, 61272219, 61021062 and 61100110), Project (No. ZZKT2013A12) supported by Key Projects Innovation Fund of State Key Laboratory, Jiangsu Planned Projects for Postdoctoral Research Funds (1601014A).
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Song, M., Sun, Z., Li, B., Hu, J. (2018). Iterative Active Classification of Large Image Collection. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10704. Springer, Cham. https://doi.org/10.1007/978-3-319-73603-7_24
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