标题：Relative similarity preserving bitwise weights generated by an adaptive mechanism
作者：Wang, Zhen; Zhang, Long-Bo; Sun, Fu-Zhen; Wang, Lei; Liu, Shu-Shu
作者机构：[Wang, Zhen; Zhang, Long-Bo; Sun, Fu-Zhen; Wang, Lei; Liu, Shu-Shu] Shandong Univ Technol, Sch Comp Sci & Technol, Zibo, Peoples R China.
通讯作者：Sun, FZ;Sun, FuZhen
通讯作者地址：[Sun, FZ]Shandong Univ Technol, Sch Comp Sci & Technol, Zibo, Peoples R China.
来源：MULTIMEDIA TOOLS AND APPLICATIONS
关键词：Binary codes; Bitwise weights; Adaptive scheme; Relative similarity; preserving
摘要：Due to its high query speed and low storage cost, binary hashing has been widely used in approximate nearest neighbors (ANN) search. However, the binary bits are generally considered to be equal, which causes data points with different codes to share the same Hamming distance to the query sample. To solve the above distance measure ambiguity, bitwise weights methods were proposed. Unfortunately, in most of the existing methods, the bitwise weights and the binary codes are learnt separately in two stages, and their performances cannot be further improved. In this paper, to effectively address the above issues, we propose an adaptive mechanism that jointly generate the bitwise weights and the binary codes by preserving different types of similarity relationship. As a result, the binary codes are utilized to obtain the initial retrieval results, and they are further re-ranked by the weighted Hamming distance. This ANN search mechanism is termed AR-Rank in this paper. First, this joint mechanism allows the bitwise weights and the binary codes to be used as mutual feedback during the training stage, and they are well adapted to one other when the algorithm converges. Furthermore, the bitwise weights are required to preserve the relative similarity which is consistent with the nature of ANN search task. Thus, the data points can be accurately re-sorted based on the weighted Hamming distances. Evaluations on three datasets demonstrate that the proposed AR-Rank retrieval system outperforms nine state-of-the-art methods.