Latent Semantic Sparse Hashing for Cross-Modal Similarity Search

Similarity search methods based on hashing for effective and efficient cross-modal retrieval on large-scale multime-dia databases with massive text and images have attracted considerable attention. The core problem of cross-modal hashing is how to effectively construct correlation between multi-modal representations which are heterogeneous intrin-sically in the process of hash function learning. Analogous to Canonical Correlation Analysis (CCA), most existing cross-modal hash methods embed the heterogeneous data into a joint abstraction space by linear projections. However, these methods fail to bridge the semantic gap more effectively, and capture high-level latent semantic information which has been proved that it can lead to better performance for im-age retrieval. To address these challenges, in this paper, w