ONION: Online Semantic Autoencoder Hashing for Cross-Modal Retrieval

Abstract

Cross-modal hashing (CMH) has recently received increasing attention with the merit of speed and storage in performing large-scale cross-media similarity search. However, most existing cross-media approaches utilize the batch-based mode to update hash functions, without the ability to efficiently handle the online streaming multimedia data. Online hashing can effectively address the preceding issue by using the online learning scheme to incrementally update the hash functions. Nevertheless, the existing online CMH approaches still suffer from several challenges, such as (1) how to efficiently and effectively utilize the supervision information, (2) how to learn more powerful hash functions, and (3) how to solve the binary constraints. To mitigate these limitations, we present a novel online hashing approach named ONION ( ON line semant I c aut O encoder hashi N g). Specifically, it leverages the semantic autoencoder scheme to establish the correlations between binary codes and labels, delivering the power to obtain more discriminative hash codes. Besides, the proposed ONION directly utilizes the label inner product to build the connection between existing data and newly coming data. Therefore, the optimization is less sensitive to the newly arriving data. Equipping a discrete optimization scheme designed to solve the binary constraints, the quantization errors can be dramatically reduced. Furthermore, the hash functions are learned by the proposed autoencoder strategy, making the hash functions more powerful. Extensive experiments on three large-scale databases demonstrate that the performance of our ONION is superior to several recent competitive online and offline cross-media algorithms.