Abstract
Substantial increase of Internet data requires efficient storage and rapid retrieval strategy. Hence, supervised hashing method is introduced in this issue. By mapping high dimensional data to compact binary codes, supervised hashing methods could downsize data while preserving semantic similarity based on labels. However, most of these hashing methods are designed for simple binary similarity, therefore they fail to manage the complex multi-level semantic structure of multi-label images. In this work, we propose a novel Multi-Label Contractive Hashing (MLCH) to preserve multi-level semantic similarity of face attributes images. To improve the efficiency of training process, an optimized triplet selection algorithm is implemented. Gradual learning is adopted to accelerate the rate of convergence and enhance the performance of proposed model. Meanwhile, contractive constraint is introduced to obtain more saturated binary codes. The proposed MLCH is evaluated on datasets CelebA and PubFig. Experimental results prove the validity of these ingenious strategies and demonstrate superiority of MLCH to the state-of-the-art hashing methods in large-scale image retrieval.
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- 1.
Proposed model does not compared to DSRH because the training code is not available.
References
Fu, X., McCane, B., Mills, S., Albert, M., Szymanski, L.: Auto-Jacobin: auto-encoder Jacobian binary hashing. CoRR abs/1602.08127 (2016)
Gong, Y., Lazebnik, S.: Iterative quantization: a procrustean approach to learning binary codes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 817–824 (2011)
Hardoon, D.R., Szedmak, S.R., Shawe-taylor, J.R.: Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16(12), 2639–2664 (2004)
Kumar, N., Belhumeur, P., Nayar, S.: FaceTracer: a search engine for large collections of images with faces. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5305, pp. 340–353. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88693-8_25
Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Attribute and simile classifiers for face verification (2010)
Liu, W., Wang, J., Ji, R., Jiang, Y.G.: Supervised hashing with kernels. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2074–2081 (2012)
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: IEEE International Conference on Computer Vision, pp. 3730–3738 (2015)
Rifai, S., Vincent, P., Muller, X., Glorot, X., Bengio, Y.: Contractive auto-encoders: explicit invariance during feature extraction. In: Getoor, L., Scheffer, T. (eds.) Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 833–840. ACM, New York (2011)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014). http://arxiv.org/abs/1409.1556
Slaney, M., Casey, M.: Locality-sensitive hashing for finding nearest neighbors [lecture notes]. IEEE Signal Process. Mag. 25, 128–131 (2008)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, pp. 1753–1760, December 2008
Zhong, Y., Sullivan, J., Li, H.: Face attribute prediction with classification CNN. CoRR abs/1602.01827 (2016). http://arxiv.org/abs/1602.01827
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Zhao, X., Jin, X., Guo, X. (2017). Face Attributes Retrieval by Multi-Label Contractive Hashing. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_29
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DOI: https://doi.org/10.1007/978-3-319-68935-7_29
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