HL-DAQ: A Dynamic Adaptive Quantization Coding for Hash Learning

The existing binary coding methods for Hash learning usually learn a set of hypergraphs for data projection, and then simply translate the result data into binary code from the division of each hyperplane. While these methods all ignore the fact that the information may be distributed unevenly in the whole projection dimension, and the range of data value in each projection dimension may not be the same. In order to solve this problem, we propose a dynamic adaptive quantization coding method called HL-DAQ, which allocates the corresponding binary coding bits to each projection dimension dynamically according to the amount of information of it. And HL-DAQ maximizes the total information of all the projections through the dynamic programming method with the purpose to preserve the neighbor structure of the original data as much as possible. Experiments prove that the dynamic adaptive quantization coding for Hash learning method proposed in this paper has significant improvement over the traditional quantization methods for Hash. It is proved that the dynamic adaptive coding for Hash learning method and the dynamic adaptive distance measurement method keep the neighbor structure of the original data better than the original quantization coding methods that fix bit and the original distance measurement method such as Hamming distance.