Multi-level supervised hashing with deep features for efficient image retrieval

Image hashing based on deep convolutional neural networks (CNN), deep hashing, has acquired breakthrough in image retrieval. Although deep features from various CNN layers have various levels of information, most of the existing deep hashing methods extract the feature vector only from the output of the penultimate fully-connected layer, focusing primarily on semantic information whilst ignoring detailed structure information. This calls for research on multi-level hashing, utilizing multi-level features to exploit different levels of CNN characteristics. To fill this gap, a novel image hashing method, Multi-Level Supervised Hashing with deep feature (MLSH), is proposed in this paper to further exploit multiple levels of deep image features. It uses a multiple-hash-table mechanism to integrate multi-level features extracted from an individual deep convolutional neural network. It takes advantage of the complementarity among multi-level features from various layers of a single deep network. High-level features reveal the semantic content of the image, while low-level features provide the structural information that is missing in high-level features. Instead of simple concatenation, several hash tables are trained individually using different levels of features from different layers, which are then integrated for efficient image retrieval. The method has been systematically evaluated through experiments on three image databases, including CIFAR-10, MNIST and NUSWIDE, and has thus been demonstrated to set a new state of the art in image hashing, outperforming several state-of-the-art hashing methods. Furthermore, the recall and precision can be balanced and improved simultaneously.