  In social networks, heterogeneous multimedia data correlate to each other,
such as videos and their corresponding tags in YouTube and image-text pairs in
Facebook. Nearest neighbor retrieval across multiple modalities on large data
sets becomes a hot yet challenging problem. Hashing is expected to be an
efficient solution, since it represents data as binary codes. As the bit-wise
XOR operations can be fast handled, the retrieval time is greatly reduced. Few
existing multimodal hashing methods consider the correlation among hashing
bits. The correlation has negative impact on hashing codes. When the hashing
code length becomes longer, the retrieval performance improvement becomes
slower. In this paper, we propose a minimum correlation regularization (MCR)
for multimodal hashing. First, the sigmoid function is used to embed the data
matrices. Then, the MCR is applied on the output of sigmoid function. As the
output of sigmoid function approximates a binary code matrix, the proposed MCR
can efficiently decorrelate the hashing codes. Experiments show the superiority
of the proposed method becomes greater as the code length increases.
