Weakly Principal Component Hashing with Multiple Tables

Image hashing based Approximate Nearest Neighbor (ANN) searching has drawn much attention in large-scale image dataset application, where balance the precision and high recall rate is difficulty task. In this paper, we propose a weakly principal component hash method with multiple tables to encode binary codes. Analyzing the distribution of projected data on different principal component directions, we find that neighbors which are far in some principal component directions maybe near in the other directions. Therefore, we construct multiple-table hashing to search the missed positive samples by previous tables, which can improve the recall. For each table, we project data to different principal component directions to learn hashing functions. In order to improve the precision rate, neighborhood points in Euclidean space should also be neighborhoods in Hamming space. So we optimize the projected data using orthogonal matrix to preserve the structure of the data in the Hamming space. Experimental and compared with six hashing results on public datasets show that the proposed method is more effective and outperforms the state-of-the-art.