Abstract
In this paper, we present an auto mobile album organization system, which can automatically classify daily photos in mobile devices into six daily categories, e.g., Baby, Food, Party, Scenery, Selfie, and Sport. Recently, deep convolutional neural networks have been used to build powerful classifiers by learning high-level representations of images. However, such models are limited to be implemented in the mobile album organization system due to the computational complexity of these networks with millions of parameters. To address these problems, we propose Mini Neural Networks (MiniNN) customized for mobile devices to improve computational efficiency of the album organization system, which is not sacrificed for performance a lot. We train the MiniNN model on the collected datasets as the classifier. Users can choose photos of any size as the input to the system, which will return the predicted tags. Experimental results show that the proposed MiniNN have an advantage over the CaffeNet with comparable classification accuracy but high computational efficiency. It can help us to organize daily photos in the mobile devices effectively and efficiently.
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Acknowledgement
This work was supported in part by the 973 Program (Project No. 2014CB347600), the National Nature Science Foundation of China (Grant No. 61672285 and 61702265) and the Natural Science Foundation of Jiangsu Province (Grant No. BK 20170856).
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Fa, L., Zhang, L., Shu, X., Song, Y., Tang, J. (2018). Mini Neural Networks for Effective and Efficient Mobile Album Organization. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_77
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