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. INDEX TERMSCorrelation Filter, support vector machine, within-class scatter, intrinsic manifold structure I. INTRODUCTION Research on computer vision algorithms has been the focus of much activity in computer science. It covers a variety of application areas and academic disciplines, including target classification, target detection, digital image processing, geometric modeling, physics, and mathematics. Among these, target classification and target detection are significant computer vision research and application areas. They are frequently utilized in our lives for vehicle detection [1], pedestrian detection [2], object recognition [3], and face recognition [4]. In the past years, correlation filters have been investigated for target classification, detection, and tracking [5] because of their noise immunity, shift-invariance, and fast training. Correlation filters achieved good performance in many pattern recognition applications including face localization, pedestrian localization, and object tracking [5]. Many computer vision methods have been combined with correlation filters. Vander et al. [6] propose matched filter (MF) which is an earlier correlation filter algorithm. The construction method of the filter is the conjugate transpose after the two-dimensional Fourier transform of the training image. The structure of the MF is too simple, only the correlation output of training images is relatively accurate, and it is not practical enough, but the application of the correlation filter in image processing also provides new ideas for subsequent researchers. After a long period of improvement, the current correlation filter can be divided into two kinds: the synthetic discriminant function filter and ...