Information Fusion via Multimodal Hashing With Discriminant Correlation Maximization
Lei Gao, Ling Guan · 2019
Due to low storage cost and fast query speed, hashing has been applied to similarity search in multimedia data widely. In this paper, an effective information fusion algorithm using multimodal hashing with discriminant correlation maximization is presented. The proposed algorithm not only finds the minimum of the semantic similarity across different modalities by multimodal hashing, but also minimizes the between-class correlation and maximizes the within-class correlation simultaneously to extract discriminant representations for information fusion. More importantly, two solutions with canonical case and non-canonical case are presented, and a novel solution to non-canonical case is proposed. Benefiting from the combination of semantic similarity across different modalities from multimodal hashing information and the discriminant representation strategy, the proposed strategy can achieve improved performance. Experimental results show that the proposed approach outperforms the related methods.