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2017 | OriginalPaper | Buchkapitel

Learning Deep Feature Fusion for Group Images Classification

verfasst von : Wenting Zhao, Yunhong Wang, Xunxun Chen, Yuanyan Tang, Qingjie Liu

Erschienen in: Computer Vision

Verlag: Springer Singapore

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Abstract

With the rapid development of social media, people tend to post multiple images under the same message. These images, we call it group images, may have very different contents, however are highly correlated in semantic space, which refers to the same theme that can be understood by a reader, easily. Understanding images present in one group has potential applications such as recommendation, user analysis, etc. In this paper, we propose a new research topic beyond the traditional image classification that aims at classifying a group of images in social media into corresponding classes. To this end, we design an end-to-end network which accepts variable number of images as input and fuses features extracted from them for classification. The method are tested on two newly collected datasets from Microblog and compared with a baseline method. The experiment demonstrates the effectiveness of our method.

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Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)

Antonio Torralba, R.F., Freeman, W.T.: 80 million tiny images: a large dataset for non-parametric object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1958–1970 (2008)CrossRef

Chang, K.Y., Liu, T.L., Lai, S.H.: From co-saliency to co-segmentation: an efficient and fully unsupervised energy minimization model. In: Computer Vision and Pattern Recognition, pp. 2129–2136 (2011)

Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

Feichtenhofer, C., Pinz, A., Zisserman, A.: Convolutional two-stream network fusion for video action recognition. In: Computer Vision and Pattern Recognition, pp. 1933–1941 (2016)

Joulin, A., Bach, F., Ponce, J.: Discriminative clustering for image co-segmentation. In: Computer Vision and Pattern Recognition, pp. 1943–1950 (2010)

Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: Computer Vision and Pattern Recognition, pp. 1725–1732 (2014)

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)

Li, H., Ngan, K.N.: A co-saliency model of image pairs. IEEE Trans. Image Process. 20, 3365–3375 (2011)MathSciNetCrossRefMATH

10.

Liao, K., Liu, G., Hui, Y.: An improvement to the sift descriptor for image representation and matching. Pattern Recogn. Lett. 34, 1211–1220 (2013)CrossRef

11.

McLaughlin, N., Martinez del Rincon, J., Miller, P.: Recurrent convolutional network for video-based person re-identification. In: Computer Vision and Pattern Recognition, pp. 1325–1334 (2016)

12.

Mukherjee, L., Singh, V., Dyer, C.R.: Half-integrality based algorithms for cosegmentation of images. In: Computer Vision and Pattern Recognition, pp. 2028–2035 (2009)

13.

Peng, X., Wang, L., Wang, X., Qiao, Y.: Bag of visual words and fusion methods for action recognition: comprehensive study and good practice. Comput. Vis. Image Underst. 150, 109–125 (2016)CrossRef

14.

Rother, C., Kolmogorov, V., Minka, T., Blake, A.: Cosegmentation of image pairs by histogram matching - incorporating a global constraint into MRFs. In: Computer Vision and Pattern Recognition, pp. 993–1000 (2006)

15.

Rubinstein, M., Joulin, A., Kopf, J., Liu, C.: Unsupervised joint object discovery and segmentation in internet images. In: Computer Vision and Pattern Recognition, pp. 1939–1946 (2013)

16.

Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems, pp. 568–576 (2014)

17.

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

18.

Tsang, I.W., Kwok, J.T., Cheung, P.M.: Core vector machines: fast SVM training on very large data sets. J. Mach. Learn. Res. 6, 363–392 (2005)MathSciNetMATH

19.

Xiao, J., Ehinger, K.A., Hays, J., Torralba, A., Oliva, A.: Sun database: exploring a large collection of scene categories. Int. J. Comput. Vision 119(1), 3–22 (2016)MathSciNetCrossRef

20.

Zhou, B., Khosla, A., Lapedriza, A., Torralba, A., Oliva, A.: Places: an image database for deep scene understanding. arXiv preprint arXiv:1610.02055 (2016)

21.

Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Advances in Neural Information Processing Systems, pp. 487–495 (2014)

Titel: Learning Deep Feature Fusion for Group Images Classification
verfasst von: Wenting Zhao
Yunhong Wang
Xunxun Chen
Yuanyan Tang
Qingjie Liu
Verlag: Springer Singapore
Buch: Computer Vision
Print ISBN: 978-981-10-7301-4

Electronic ISBN: 978-981-10-7302-1

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-981-10-7302-1_47

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