  Recent years have seen the exponential growth of heterogeneous multimedia
data. The need for effective and accurate data retrieval from heterogeneous
data sources has attracted much research interest in cross-media retrieval.
Here, given a query of any media type, cross-media retrieval seeks to find
relevant results of different media types from heterogeneous data sources. To
facilitate large-scale cross-media retrieval, we propose a novel unsupervised
cross-media hashing method. Our method incorporates local affinity and distance
repulsion constraints into a matrix factorization framework. Correspondingly,
the proposed method learns hash functions that generates unified hash codes
from different media types, while ensuring intrinsic geometric structure of the
data distribution is preserved. These hash codes empower the similarity between
data of different media types to be evaluated directly. Experimental results on
two large-scale multimedia datasets demonstrate the effectiveness of the
proposed method, where we outperform the state-of-the-art methods.
