Binary coding by matrix classifier for efficient subspace retrieval

Abstract

Fast retrieval in large-scale database with high-dimensional subspaces is an important task in many applications, such as image retrieval, video retrieval and visual recognition. This can be facilitated by approximate nearest subspace (ANS) retrieval which requires effective subspace representation. Most of the existing methods for this problem represent subspace by point in the Euclidean space or the Grassmannian space before applying the approximate nearest neighbor (ANN) search. However, the efficiency of these methods can not be guaranteed because the subspace representation step can be very time consuming when coping with high dimensional data. Moreover, the transforming process for subspace to point will cause subspace structural information loss which influence the retrieval accuracy. In this paper, we present a new approach for hashing-based ANS retrieval. The proposed method learns the binary codes for given subspace set following a similarity preserving criterion. It simultaneously leverages the learned binary codes to train matrix classifiers as hash functions. This method can directly binarize a subspace without transforming it into a vector. Therefore, it can efficiently solve the large-scale and high-dimensional multimedia data retrieval problem. Experiments on face recognition and video retrieval show that our method outperforms several state-of-the-art methods in both efficiency and accuracy.