Efficient Hash Code Expansion by Recycling Old Bits

Deep hashing methods have been intensively studied and successfully applied in large-scale multimedia retrieval. In real-world scenarios, code length can not be set once for all if retrieval accuracy is not satisfying. However, when code length increases, conventional deep hashing methods have to retrain their models and regenerate the whole database codes, which is impractical for large-scale retrieval system. In this paper, we propose an interesting deep hashing method from a brand new perspective, called Code Expansion oriented Deep Hashing (CEDH). Different from conventional deep hashing methods, our CEDH focuses on the fast expansion of existing hash codes. Instead of regenerating all bits from raw images, the new bits in CEDH can be incrementally learned by recycling the old ones. Specifically, we elaborately design an end-to-end asymmetric framework to simultaneously optimize a CNN model for query images and a code projection matrix for database images. With the learned code projection matrix, hash codes can achieve fast expansion through simple matrix multiplication. Subsequently, a novel code expansion hashing loss is proposed to preserve the similarities between query codes and expanded database codes. Due to the loose coupling in our framework, our CEDH is compatible with a variety of deep hashing methods. Moreover, we propose to adopt smooth similarity matrix to solve "similarity contradiction" problem existing in multi-label image datasets, thus further improving our performance on multi-label datasets. Extensive experiments on three widely used image retrieval benchmarks demonstrate that CEDH can significantly reduce the cost for expanding database codes (about 100,000x faster with GPU and 1,000,000x faster with CPU) when code length increases while keeping the state-of-the-art retrieval accuracy. Our code is available at https://github.com/IIE-MMR/2022MM-CEDH.