Abstract
Sharing images online poses security threats to a wide range of users due to the unawareness of privacy information. Deep features have been demonstrated to be a powerful representation for images. However, deep features usually suffer from the issues of a large size and requiring a huge amount of data for fine-tuning. In contrast to normal images (e.g., scene images), privacy images are often limited because of sensitive information. In this paper, we propose a novel approach that can work on limited data and generate deep features of smaller size. For training images, we first extract the initial deep features from the pre-trained model and then employ the K-means clustering algorithm to learn the centroids of these initial deep features. We use the learned centroids from training features to extract the final features for each testing image and encode our final features with the triangle encoding. To improve the discriminability of the features, we further perform the fusion of two proposed unsupervised deep features obtained from different layers. Experimental results show that the proposed features outperform state-of-the-art deep features, in terms of both classification accuracy and testing time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chollet, F.: Xception: deep learning with depthwise separable convolutions. arXiv preprint, pp. 1610–02357 (2017)
Chollet, F., Allaire, J.: R interface to Keras. https://github.com/rstudio/keras
Coates, A., Ng, A., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: AISTATS, pp. 215–223 (2011)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR (2009)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.: Densely connected convolutional networks. In: CVPR, vol. 1, p. 3 (2017)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)
Lin, T.Y., RoyChowdhury, A., Maji, S.: Bilinear CNN models for fine-grained visual recognition. In: ICCV, pp. 1449–1457 (2015)
Lin, T.Y., RoyChowdhury, A., Maji, S.: Bilinear convolutional neural networks for fine-grained visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1309–1322 (2017)
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2014)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Sitaula, C., Xiang, Y., Basnet, A., Aryal, S., Lu, X.: Tag-based semantic features for scene image classification. arXiv preprint arXiv:1909.09999 (2019)
Sitaula, C., Xiang, Y., Zhang, Y., Lu, X., Aryal, S.: Indoor image representation by high-level semantic features. IEEE Access 7, 84967–84979 (2019)
Spyromitros-Xioufis, E., Papadopoulos, S., Popescu, A., Kompatsiaris, Y.: Personalized privacy-aware image classification. In: ICMR, pp. 71–78 (2016)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI, vol. 4, p. 12 (2017)
Szegedy, C., et al.: Going deeper with convolutions. In: CVPR, pp. 1–9 (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR, pp. 2818–2826 (2016)
Tonge, A., Caragea, C.: Privacy prediction of images shared on social media sites using deep features. arXiv preprint arXiv:1510.08583 (2015)
Tonge, A., Caragea, C.: On the use of deep features for online image sharing. In: Companion of the The Web Conference, pp. 1317–1321 (2018)
Tonge, A., Caragea, C., Squicciarini, A.C.: Uncovering scene context for predicting privacy of online shared images. In: AAAI, pp. 8167–8168 (2018)
Tonge, A.K., Caragea, C.: Image privacy prediction using deep features. In: AAAI, pp. 4266–4267 (2016)
Tran, L., Kong, D., Jin, H., Liu, J.: Privacy-CNH: a framework to detect photo privacy with convolutional neural network using hierarchical features. In: AAAI, pp. 1317–1323 (2016)
Yang, J., Yang, J., Zhang, D., Lu, J.F.: Feature fusion: parallel strategy vs. serial strategy. Pattern Recognit. 36(6), 1369–1381 (2003)
Zerr, S., Siersdorfer, S., Hare, J.: Picalert!: a system for privacy-aware image classification and retrieval. In: CIKM, pp. 2710–2712. ACM (2012)
Zerr, S., Siersdorfer, S., Hare, J., Demidova, E.: Privacy-aware image classification and search. In: SIGIR, pp. 35–44 (2012)
Zhong, H., Squicciarini, A., Miller, D., Caragea, C.: A group-based personalized model for image privacy classification and labeling. In: IJCAI, pp. 3952–3958 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sitaula, C., Xiang, Y., Aryal, S., Lu, X. (2019). Unsupervised Deep Features for Privacy Image Classification. In: Lee, C., Su, Z., Sugimoto, A. (eds) Image and Video Technology. PSIVT 2019. Lecture Notes in Computer Science(), vol 11854. Springer, Cham. https://doi.org/10.1007/978-3-030-34879-3_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-34879-3_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34878-6
Online ISBN: 978-3-030-34879-3
eBook Packages: Computer ScienceComputer Science (R0)