Hashing has shown great potential in large-scale image retrieval due to its storage and computation efficiency, especially the recent deep supervised hashing methods. To achieve promising performance, deep supervised hashing methods require a large amount of training data from different classes. However, when images of new categories emerge, existing deep hashing methods have to retrain the CNN model and generate hash codes for all the database images again, which is impractical for large-scale retrieval system. In this paper, we propose a novel deep hashing framework, called Deep Incremental Hashing Network (DIHN), for learning hash codes in an incremental manner. DIHN learns the hash codes for the new coming images directly, while keeping the old ones unchanged. Simultaneously, a deep hash function for query set is learned by preserving the similarities between training points. Extensive experiments on two widely used image retrieval benchmarks demonstrate that the proposed DIHN framework can significantly decrease the training time while keeping the state-of-the-art retrieval accuracy.

Deep supervised hashing methods for multi-label image retrieval have achieved great success nowadays. However, these methods only take the similarity between the database images and the query images into account, but they ignore the uniqueness of the database images when deciding on their rankings. Here we present a novel Deep Uniqueness-Aware Hashing (DUAH) method for learning hash functions that preserve not only multilevel semantic similarity between multi-label images, but also the unique semantic structure of each image. In our approach, both the pairwise label information and the classification information are fully exploited to maximize the discriminability of the output binary codes within one stream framework. Extensive evaluations conducted on three widely used multi-label image benchmarks demonstrate that DUAH can support fine-grained multi-label image retrieval better.

Deep hashing methods have achieved promising results for large-scale image retrieval recently. To accelerate the subsequent Hamming ranking process, the multi-index approach has been proposed to reduce the computations for the Hamming distance. However, the binary codes output by the previous deep hashing methods may not be optimally compatible with the multi-index approach. In this paper, we present a novel Deep Index-Compatible Hashing (DICH) method for fast image retrieval, which can learn similarity-preserving binary codes that are more compatible with the multi-index approach. With the learned binary codes, both the size of the intermediate result set produced by the multi-index approach and the number of the candidate images can be reduced, which can accelerate the Hamming ranking process. By taking advantage of the unique feature of DICH, we further propose a block-based ranking strategy to quickly rank the candidate images without calculating the Hamming distance. Extensive evaluations demonstrate that the proposed method can significantly reduce the retrieval time with almost no loss of retrieval accuracy.