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Constructing Adaptive Correlation Filters Based on Dictionary Learning
Cui, Xiaoguang; Tian, Yuan
Conference NameThe International Conference on Management Innovation and Information Technology
Source PublicationWIT Transactions on Information and Communication Technologies, Volume 61
Conference Date2013-12
Conference PlaceWu Han
AbstractIn this paper, we present a novel model for constructing adaptive correlation filters based on dictionary learning.  Rather than produce a single general filter for the entire testing set, we use recently developed dictionary learning techniques to generate a filter bank, by which we can construct an accurate adaptive correlation filter for each testing image via sparse coding. With the powerful representation of the learned filter bank, the greater accuracy with regard to varied appearance changes can be achieved. In addition, to meet the real-time requirement, a non-linear regress method that directly maps input images into corresponding filters is proposed as a substitute for the time-consuming sparse coding. Experiments were performed in optical satellite images to detect airplanes, and our model shows significant reduction in error rate compared to the classical Average of Synthetic Exact Filters (ASEF) and Minimum Output Sum of Squared Error (MOSSE) filters.
KeywordCorrelation Filters Dictionary Learning Sparse Coding Non-linear Regress Synthetic Exact Filters
Indexed ByEI
Document Type会议论文
Corresponding AuthorCui, Xiaoguang
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Cui, Xiaoguang,Tian, Yuan. Constructing Adaptive Correlation Filters Based on Dictionary Learning[C],2013.
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