Maximum Margin Correlation Filter Based on Data Spatial Distribution Information

Abstract

In the past decade, object localization and object classification using correlation filters, especially large margin correlation filters which combine with support vector machine (SVM), have become a hotspot. However, the large margin correlation filters do not consider the class distribution and the structural features within the class during training, which is easily affected by noise. This paper presents two methods to overcome this drawback: minimum class variance large margin correlation filter (MCVLMCF) and minimum class locality preserving variance correlation filter (MCLPVCF). First, the overall structure information of the target is obtained by the within-class scatter with MCVLMCF, and the spatial features of the sample are extracted by the intrinsic manifold structure of data with MCLPVCF. Then, we embed these two types of information into the optimal function of the large margin correlation filter, fuse the sample spatial features with the large margin principle and correlation filtering, and convert it to solve the filter in the frequency domain. Finally, object localization experiments in actual environments and classification experiments on different datasets demonstrate that our proposed methods can adapt to complex object changes and achieve better performances than some state-of-the-art methods.