双重结构保持的在线跨模态哈希

Online Cross-Modal Hashing with Double Structure Preserving

近年来,在线跨模态哈希因其能处理更为贴近现实的流数据场景而受到广泛关注.虽然取得了不错的进展,但现有方法大都依赖准确清晰的数据标记.目前,针对无监督学习模式下在线跨模态哈希的研究相对较少,还有很多问题有待解决.例如,新到达的数据流通常规模较小,因此常常存在分布不平衡的现象.而现有模型极少关注这个问题,导致模型对离群样本敏感,鲁棒性较差.并且,现有方法大都关注样本的整体结构而忽视了邻域信息对于生成公共哈希码的帮助.为了解决上述问题,提出了基于双重结构保持的无监督在线跨模态哈希方法,称为SPOCH(structure preserving online cross-modal hashing).该方法的基本思想是同时挖掘样本空间的全局结构信息和邻域结构信息来生成相应的公共表示,用以指导哈希码和哈希函数的学习.针对全局结构的学习,引入L_(2,1)范数取代L_(2)范数来约束损失函数,利用L_(2,1)范数结构化稀疏的性质缓解模型对离群样本的敏感性;针对邻域结构的学习,利用多模态融合的邻域样本进行样本重构,使得所学公共表示更好地表征多模态信息.此外,为了缓解遗忘问题,提出在新旧数据上联合优化,并设计相应的更新策略提高算法的训练效率,实现在线检索.在2个广泛使用的跨模态检索数据集上进行的实验结果表明,较现有最先进的无监督在线跨模态哈希方法,SPOCH在可比较甚至更短的训练时间内取得了更优的检索精度,验证了所提方法的有效性.

Online cross-modal hashing has received increasing attention due to its efficiency and effectiveness in handling cross-modal streaming data retrieval.Despite promising progress,most existing methods rely on accurate and clear supervised information.There are only a few online cross-modal hashing studies concentrating on unsupervised learning mode,and numerous challenges still need to be tackled.For example,streaming data usually suffer from the unbalanced distribution problem due to the limited volume of data in each chunk.Most existing methods neglect this problem,resulting in heightened sensitivity to outlier samples and compromised robustness.Moreover,existing models typically exploit global data distribution,while ignoring local neighborhood information that can promote hash learning.To solve these problems,we propose an unsupervised online cross-modal hashing method with double structure-preserving,called SPOCH(structure preserving online cross-modal hashing).It simultaneously explores the global structure and local structure to generate the corresponding common representation;thereafter,the learned common representation can be used to guide the hash learning process.In terms of global structure-preserving,we design the loss function based on L_(2,1)norm,which can alleviate the sensitivity of outlier samples.In terms of local structure-preserving,we reconstruct sample representation based on neighbor relations that integrates the multi-modality information.In addition,to alleviate the forgetting problem,we propose joint optimization on streaming data,and design the corresponding update strategy to improve the training efficiency.We conduct experiments on two widely-used cross-modal retrieval datasets.Compared with the existing state-of-the-art unsupervised online cross-modal hashing methods,SPOCH achieves superior retrieval accuracy within a comparable or even shorter training time,validating the effectiveness of the proposed approach.