  The key challenge of unsupervised vehicle re-identification (Re-ID) is
learning discriminative features from unlabelled vehicle images. Numerous
methods using domain adaptation have achieved outstanding performance, but
those methods still need a labelled dataset as a source domain. This paper
addresses an unsupervised vehicle Re-ID method, which no need any types of a
labelled dataset, through a Self-supervised Metric Learning (SSML) based on a
feature dictionary. Our method initially extracts features from vehicle images
and stores them in a dictionary. Thereafter, based on the dictionary, the
proposed method conducts dictionary-based positive label mining (DPLM) to
search for positive labels. Pair-wise similarity, relative-rank consistency,
and adjacent feature distribution similarity are jointly considered to find
images that may belong to the same vehicle of a given probe image. The results
of DPLM are applied to dictionary-based triplet loss (DTL) to improve the
discriminativeness of learnt features and to refine the quality of the results
of DPLM progressively. The iterative process with DPLM and DTL boosts the
performance of unsupervised vehicle Re-ID. Experimental results demonstrate the
effectiveness of the proposed method by producing promising vehicle Re-ID
performance without a pre-labelled dataset. The source code for this paper is
publicly available on `https://github.com/andreYoo/VeRI_SSML_FD.git'.
