Supervised Class Graph Preserving Hashing for Image Retrieval and Classification

With the explosive growth of data, hashing-based techniques have attracted significant attention due to their efficient retrieval and storage reduction ability. However, most hashing methods do not have the ability of predicting the labels directly. In this paper, we propose a novel supervised hashing approach, namely Class Graph Preserving Hashing (CGPH), which can well incorporate label information into hashing codes and classify the samples with binary codes directly. Specifically, CGPH learns hashing functions by ensuring label consistency and preserving class graph similarity among hashing codes simultaneously. Then, it learns effective binary codes through orthogonal transformation by minimizing the quantization error between hashing function and binary codes. In addition, an iterative method is proposed for the optimization problem in CGPH. Extensive experiments on two large scale real-world image data sets show that CGPH outperforms or is comparable to state-of-the-art hashing methods in both image retrieval and classification tasks.