Deep Hashing With Walsh Domain for Multi-Label Image Retrieval

Yinqi Chen, Peiwen Li, Yangting Zheng, Weijian Luo, Xiang Gao

Abstract: The existing deep hashing methods for image retrieval typically modeling hash-coding layer in real number space. However, these methods frequently overlook the intrinsic information loss that occurs during the hash-coding process, as the hash layer performs two tasks simultaneously: spatial transformation and dimensionality reduction. Especially in multi-label image retrieval, the exponential increase in the number of combinations with the labels further amplifies the information loss in Hamming space. Consequently, the efficiency of the hash-coding is unsatisfactory. To mitigate this limitation, we introduce a novel approach termed WalshHash, which is grounded in the principles of Walsh transformation in signal processing. Unlike conventional techniques, WalshHash formulates the hash-coding layer as a filtering process based on Kolmogorov-Arnold Networks (KANs) in the Walsh domain accompanied by constraint loss functions on multiple domains. It ensures the dimensionality reduction in the Walsh domain can be effectively projected onto the real number domain with minimal information loss, because the Walsh space encapsulates the critical information components. As a result, WalshHash demonstrates superior performance in multi-label image retrieval compared to State-of-the-Art (SOTA) methods.

External IDs:dblp:journals/spl/ChenLZLG25