Supervised Online Hashing via Similarity Distribution Learning

Hashing based visual search has attracted extensive research Online hashing has attracted extensive research attention attention in recent years due to the rapid growth of when facing streaming data. Most online hashing visual data on the Internet [7, 33, 8, 26, 12, 13, 30, 32, 25, methods, learning binary codes based on pairwise similarities 35, 27]. In various scenarios, online hashing has become of training instances, fail to capture the semantic relationship, a hot topic due to the emergence of handling the streaming and suffer from a poor generalization in largescale data, which aims to resolve an online retrieval task by applications due to large variations. In this paper, we updating the hash functions from sequentially arriving data propose to model the similarity distributions between the input instances. On one hand, online hashing takes advantages data and the hashing codes, upon which a novel supervised of traditional offline hashing methods, i.e., low storage cost online hashing method, dubbed as Similarity Distribution and efficiency of pairwise distance computation in the Hamming based Online Hashing (SDOH), is proposed, to keep space. On the other hand, it also merits in training the intrinsic semantic relationship in the produced Hamming efficiency and scalability for large-scale applications, since space. Specifically, we first transform the discrete the hash functions are updated instantly and solely based on similarity matrix into a probability matrix via a Gaussianbased the current streaming data, which is superior to traditional normalization to address the extremely imbalanced hashing methods based on a hashing model entirely trained distribution issue. And then, we introduce a scaling Student from scratch.