Deep Hashing with Hash-Consistent Large Margin Proxy Embeddings
This work addresses the challenge of producing effective binary hash codes for image retrieval and classification, which is incremental as it builds on existing proxy embedding methods.
The paper tackled the problem of binarizing proxy embeddings for image hashing by introducing hash-consistent large margin proxies to eliminate rotational ambiguity and encourage saturation, resulting in highly discriminative hash-codes with small binarization error. Experiments showed that the semantic extension (sHCLM) achieved significant improvements over state-of-the-art methods on multiple datasets, both within and beyond training classes.
Image hash codes are produced by binarizing the embeddings of convolutional neural networks (CNN) trained for either classification or retrieval. While proxy embeddings achieve good performance on both tasks, they are non-trivial to binarize, due to a rotational ambiguity that encourages non-binary embeddings. The use of a fixed set of proxies (weights of the CNN classification layer) is proposed to eliminate this ambiguity, and a procedure to design proxy sets that are nearly optimal for both classification and hashing is introduced. The resulting hash-consistent large margin (HCLM) proxies are shown to encourage saturation of hashing units, thus guaranteeing a small binarization error, while producing highly discriminative hash-codes. A semantic extension (sHCLM), aimed to improve hashing performance in a transfer scenario, is also proposed. Extensive experiments show that sHCLM embeddings achieve significant improvements over state-of-the-art hashing procedures on several small and large datasets, both within and beyond the set of training classes.