Abstract
Correlation filters have attracted growing attention due to their high efficiency, which have been well studied for binary classification. However, by setting the desired output to be a fixed Gaussian function, the conventional multi-class classification based on correlation filters becomes problematic due to the under-fitting in many real-world applications. In this paper, we propose an adaptive multi-class correlation filters (AMCF) method based on an alternating direction method of multipliers (ADMM) framework. Within this framework, we introduce an adaptive output to alleviate the under-fitting problem in the ADMM iterations. By doing so, a closed-form sub-solution is obtained and further used to constrain the optimization objective, simplifying the entire inference mechanism. The proposed approach is successfully combined with the Histograms of Oriented Gradients (HOG) features, multi-channel features and convolution features, and achieves superior performances over state-of-the-arts in two multi-class classification tasks including handwritten digits recognition and RGBD-based action recognition.
This work was supported in part by the Natural Science Foundation of China under Contract 61272052 and Contract 61473086, in part by PAPD, in part by CICAEET, and in part by the National Basic Research Program of China under Grant 2015CB352501. The work of B. Zhang was supported by the Program for New Century Excellent Talents University within the Ministry of Education, China, and Beijing Municipal Science & Technology Commission Z161100001616005.
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Yang, L., Chen, C., Wang, H., Zhang, B., Han, J. (2016). Adaptive Multi-class Correlation Filters. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_67
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