Sparse constraint nearest neighbour selection in cross-media retrieval

With the rapid increasing multimedia documents including videos, images or text, the cross-media retrieval is being focused on. Currently, most of state-of-art methods belonging to the retrieval methods are developed within the scope of the transductive learning. And as soon as the query samples are outside the database, k-nearest-neighbor method is always adopted. However, under such circumstances the fixed global parameter k is not robust for all queries with diverse semantics. In this paper, we propose an alternative method based on the sparse representation. The query sample is considered as a sparse linear combination of all training samples, and the number of nearest neighbors is determined automatically according to the sparse coefficients to the query. Then we import the selection of nearest neighbors into a cross-media ranking model with Local Regression and Global Alignment (LRGA) to get the relevant documents to the query. We conduct extensive experiments for cross-media retrieval to demonstrate the efficiency and effectiveness of our methods.