Abstract
In this paper, we propose a Coupled Discriminative Dictionary Learning framework to tackle the zero-shot image classification problem. Instead of the original attribute vectors and sample feature vectors, we use their corresponding sparse coefficients attained from sparse coding to do the classification. The purpose of our framework is that, when an unseen-class sample shows during test time, we first attain its corresponding sparse coefficient through learned feature dictionary. Then we use a mapping method to map it to the attribute sparse coefficients category histogram domain where we can accomplish the classification. We evaluate our method performance on two benchmark datasets for zero-shot image classification. The results are compelling to other state-of-the-art, especially on fine-grained dataset.
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Liu, L., Wu, S., Chen, R., Zhou, M. (2017). Zero-Shot Image Classification via Coupled Discriminative Dictionary Learning. In: Yue, D., Peng, C., Du, D., Zhang, T., Zheng, M., Han, Q. (eds) Intelligent Computing, Networked Control, and Their Engineering Applications. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 762. Springer, Singapore. https://doi.org/10.1007/978-981-10-6373-2_37
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DOI: https://doi.org/10.1007/978-981-10-6373-2_37
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