Robust online hashing with label semantic enhancement for cross-modal retrieval

Abstract

Online hashing technology has attracted extensive attention owing to its effectiveness and efficiency in processing large-scale streaming data. However, there are still some limitations: (1) In practical applications, the observed labels of multimedia data are obtained through manual annotation, which may inevitably introduce some noises into labels. This may lead to retrieval performance degradation when the noisy labels are directly applied to retrieval tasks. (2) The potential semantic correlation of multi-labels cannot be fully explored. To overcome these limitations, in this paper, we propose robust online hashing with label semantic enhancement (ROHLSE). Specifically, ROHLSE seeks to recover the clean labels from the provided noisy labels by imposing low-rank and sparse constraints. Meanwhile, it employs the representation of samples in the feature space to predict the labels via the dependency between sample instances and labels. To efficiently handle streaming data, ROHLSE preserves the similarity between new data, and establishes the semantic relationships between new and old data through chunk similarity, simultaneously. Furthermore, ROHLSE can fully utilize the semantic correlations between multiple labels of each instance. Extensive experiments are conducted on three benchmark datasets to demonstrate the superiority of the proposed ROHLSE approach.