Supervised hashing for multi-labeled data with order-preserving feature

Approximate Nearest Neighbors (ANN) Search has attracted much attention in recent years. Hashing is a promising way for ANN which has been widely used in large-scale image retrieval tasks. However, most of the existing hashing methods are designed for single-labeled data. On multi-labeled data, those hashing methods take two images as similar if they share at least one common label. But this way cannot preserve the order relations in multi-labeled data. Meanwhile, most hashing methods are based on hand-crafted features which are costing. To solve the two problems above, we proposed a novel supervised hashing method to perform hash codes learning for multi-labeled data. In particular, we firstly extract the order-preserving data features through deep convolutional neural network. Secondly, the order-preserving features would be used for learning hash codes. Extensive experiments on two real-world public datasets show that the proposed method outperforms state-of-the-art baselines in the image retrieval tasks.