  Hashing has been recognized as an efficient representation learning method to
effectively handle big data due to its low computational complexity and memory
cost. Most of the existing hashing methods focus on learning the
low-dimensional vectorized binary features based on the high-dimensional raw
vectorized features. However, studies on how to obtain preferable binary codes
from the original 2D image features for retrieval is very limited. This paper
proposes a bilinear supervised discrete hashing (BSDH) method based on 2D image
features which utilizes bilinear projections to binarize the image matrix
features such that the intrinsic characteristics in the 2D image space are
preserved in the learned binary codes. Meanwhile, the bilinear projection
approximation and vectorization binary codes regression are seamlessly
integrated together to formulate the final robust learning framework.
Furthermore, a discrete optimization strategy is developed to alternatively
update each variable for obtaining the high-quality binary codes. In addition,
two 2D image features, traditional SURF-based FVLAD feature and CNN-based
AlexConv5 feature are designed for further improving the performance of the
proposed BSDH method. Results of extensive experiments conducted on four
benchmark datasets show that the proposed BSDH method almost outperforms all
competing hashing methods with different input features by different evaluation
protocols.
