Key Points Centered Sparse Hashing for Cross-Modal Retrieval

Supervised cross-modal hashing methods usually construct a massive undirected weighted graph based on labels for training data, with the aim of learning more structured hash codes by preserving relationships within this graph. However, as the volume of data increases, such an approach demands substantial computational and storage resources and tends to aggregate all data points with paths, even semantically unrelated ones, which undermines the retrieval performance. In this paper, we propose to prune less crucial paths from this graph to obtain a clearer representation of relationships among data points. This not only reduces computational resources but separates semantically unrelated data points. Specifically, we define key points within the graph and retain relationships only between all data points and these key points, resulting in a simplified and more transparent graph that is used to supervise hash code learning. Experimental results on three datasets demonstrate that removing unimportant paths from the relationship graph can lead to the learning of more structured hash codes, thereby improving retrieval performance.