Abstract
During the past few years, single-label classification has been extensively studied. However, in public datasets, the number of multiple-labeled images is much larger than the number of single-labeled images, which means that the study of multi-label image classification is more important. Most of the published network for multi-label image classification uses a CNN with a sigmoid layer, which is different from the single-label classification network using a CNN with a softmax layer. The binary cross entropy is often used as loss function for multi-label image classification. But due to the complex underlying object layout and feature confusion caused by multiple tags, the effect of CNN with a sigmoid layer on multi-label image classification is not satisfactory. Recently, in order to break some restrictions of CNN, the concept of capsule networks has been proposed. In this paper, a capsule network layer has been used to replace the traditional fully-connected layer and the sigmoid layer in the CNN network to improve the effect of multi-label image classification. In order to solve the deep network’s convergence problem due to insufficient training data, fine-tuning DCNNs techniques have been applied to the capsule network architecture. In the experiments, three datasets, PASCAL VOC 2007, PASCAL VOC 2012 and NUS-WIDE, have been used. The proposed CNN+Capsule architecture has been compared with the traditional CNN+FullyConnected architecture. It has been shown that with different parameter settings the proposed CNN+Capsule architecture can consistently achieve better performance than the CNN+FullyConnected architecture.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Deng, J., Dong, W., Socher, R., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2009)
MartĂnez, A., Benavente, R.: The AR face database. CVC Technical report (1998)
Zhao, W., Chellappa, R., Krishnaswamy, A.: Discriminant analysis of principal components for face recognition. In: IEEE International Conference on Automatic Face & Gesture Recognition (2002)
LeCun, Y., Cortes, C., Burges, C.J.C.: The MNIST database of handwritten digits (1998)
Cao, J., Ahmadi, M., Shridhar, M.: Recognition of handwritten numerals with multiple feature and multistage classifier. Pattern Recogn. 28(2), 153–160 (1995)
Gong, Y., Jia, Y., Leung, T., et al.: Deep convolutional ranking for multilabel image annotation (2013)
Wei, Y., Xia, W., Lin, M., et al.: HCP: a flexible CNN framework for multi-label image classification. IEEE Trans. Pattern Anal. Mach. Intell. 38, 1901–1907 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., et al.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Song, Z., Chen, Q., Huang, Z., et al.: Contextualizing object detection and classification (2011)
Dong, J., Xia, W., Chen, Q., et al.: Subcategory-aware object classification. In: IEEE 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)
Oquab, M., Bottou, L., Laptev, I., et al.: Learning and transferring mid-level image representations using convolutional neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society (2014)
Wang, J., Yang, Y., Mao, J., et al.: CNN-RNN: a unified framework for multi-label image classification (2016)
Cheng, M.M., Liu, Y., Lin, W.Y., et al.: BING: binarized normed gradients for objectness estimation at 300 frames per second. Comput. Vis. Media 5, 3–20 (2018)
Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 391–405. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_26
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules (2017)
Xi, E., Bing, S., Jin, Y.: Capsule network performance on complex data (2017)
Afshar, P., Mohammadi, A., Plataniotis, K.N.: Brain tumor type classification via capsule networks (2018)
Srihari, S.: Capsule Nets (PDF). University of Buffalo (2017)
Chollet, F.: Xception: deep learning with depthwise separable convolutions (2016)
Everingham, M., Van Gool, L., Williams, C.K., et al.: The pascal visual object classes (VOC) challenge (2010)
Chua, T.S., Tang, J., Hong, R., et al.: NUS-WIDE: a real-world web image database from national university of Singapore. In: Proceedings of the ACM International Conference on Image and Video Retrieval, p. 48. ACM (2009)
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
Pan, D., Lu, Y., Kang, P. (2019). A Deep Learning Model for Multi-label Classification Using Capsule Networks. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-26763-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26762-9
Online ISBN: 978-3-030-26763-6
eBook Packages: Computer ScienceComputer Science (R0)