Least square regularized spectral hashing for similarity search

Among the existing hashing methods, spectral hashing (SpH) and self-taught hashing (STH) are considered as the state-of-the-art works. However, two such methods still have some drawbacks. For example, when generating the extension of out-of-sample, SpH makes assumption that data follows uniform distribution but it is impractical. As to STH, its hash functions are obtained by training SVM classifier bit-by-bit, which will lead to ten-fold increase in training time. Moreover, they both suffer overfitting issue. To conquer those drawbacks, we propose a new hashing method, also called LS_SPH, which adopts a unified objective function to obtain the binary embeddings of training objects and hash functions for predicting hash code of test object. Integrating two such processes together will bring in two advantages: (1) It can highly decrease the time complexity of offline stage for training hash codes and hash function due to not requiring extra time for learning hash function. (2) The overfitting issue can be successfully avoided because the empirical loss function associated with hash function is served as the regularization item in objective function in this method. The extensive experiments show that the LS_SPH is superior to the state-of-the-art hashing methods such as SpH and STH on the whole.

► We adopt a unified function to generate hash codes for training and test data.
► Integrating two such processes together decreases the time complexity of training.
► Integrating two such processes together successfully avoids the overfitting issue.
► The proposed method is competitive with the state-of-the-art methods in accuracy.