Adaptive Multi-class Correlation Filters

Linlin Yang, Chen Chen, Hainan Wang, Baochang Zhang, Jungong Han

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

6 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing - PCM 2016 :17th Pacific-Rim Conference on Multimedia, Xi´ an, China, September 15-16, 2016
EditorsEnqing Cheng, Yihong Gong, Yun Tie
ISBN (Print)978-3-319-48895-0
Publication statusPublished - 27 Nov 2016
Event17th Pacific-Rim Conference on Multimedia - Xi´ an, China
Duration: 27 Nov 2016 → …

Publication series

NameLecture Notes in Computer Science
ISSN (Electronic)0302-9743


Conference17th Pacific-Rim Conference on Multimedia
Period27/11/16 → …


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