Car re-identification from large scale images using semantic attributes

Car re-identification, searching a specific car object from a large-scale car image database, is investigated in this paper. Previous work mainly focuses on fixed pose and overlooks the special appearance. However, avoiding matching other poses would lead to coarse results of the car retrieval. And some special attributes like individual paintings which are greatly helpful for car retrieval have not drawn enough attention. This paper addresses these problems through multi-poses matching and re-ranking based on special attributes. Our core idea lies in query expansion method that can capture weighted attributes to build the retrieval model, which allows us to estimate invisible attributes by the visible ones to construct complete attributes vectors to car retrieval in any poses. Furthermore, we divide all attributes into two groups, special attributes and common attributes. Here special attributes represent the abnormal appearance like individual paintings or car damage while common attributes denote the intrinsic appearance of car. Using special attributes to re-rank results turns out to be beneficial to improve the retrieval performance. In the end, the experiments demonstrate the effectiveness of our approach on the car datasets.