Robust vehicle logo recognition based on locally collaborative representation with principal components

Abstract

Vehicle logo recognition (VLR) is a main issue in vehicle identification system. Logo recognition is still a challenge technique since VLR methods suffer from the large within-class variations due to the different illumination conditions, different viewpoints et al. In this paper, motivated by the excellent performance of the collaborative representation based classification (CRC), we formulate VLR problem under CRC scheme. It is noted that the performance of CRC is generally proportional to the size of the dictionary for better data representation capability. However, a large dictionary requires high computational cost. Aiming at maintaining the CRC performance but reducing the cost, the principal components analysis (PCA) is firstly employed on the class-dictionary to remove within-class information redundancy and noisy components. In addition, we introduce a new idea to code a data over a local dictionary instead of a global dictionary used in a conventional CRC, where the local dictionary is built by selecting the K-nearset neighbors of this data. As a result, a novel locally collaborative representation based classification with principal components (termed as LCRC_PC) method is systematically derived. The proposed LCRC_PC method is evaluated on two data sets. The average accuracies are 99.44% and 99.53% on a self-built data set and a public data set, respectively. Moreover, the computational cost of LCRC_PC is about a tenth of that of conventional CRC. Experimental results validate the effectiveness and robustness of our proposed LCRC_PC method.