Supervised cross-modal hashing has attracted many researchers. In these studies, they seek a common semantic space or directly regress the zero-one label information into the Hamming space. Although they achieve many achievements, they neglect some issues: 1) some methods of the classification task are not suitable for retrieval tasks, since they are lack of learning personalized features of sample; 2) the outcomes of hash retrieval are related to both the length and encoding method of hash codes. Because a sample possess more personalized features than label semantics, in this paper, we propose a novel supervised cross-modal hashing collaboration learning method called discrete Cross-modal Hashing with Relaxation and Label Semantic Guidance (CHRLSG). First, we introduce two relaxation variables as latent spaces. One is used to extract text features and label semantic information collaboratively, and the other is used to extract image features and label semantics collaboratively. Second, the more accurate hash codes are generated from latent spaces, since CHRLSG learns collaboratively feature semantics and label semantics by using labels as the domination and features as the auxiliary. Third, we utilize labels to strengthen the similar relationship of inter-modal samples via keeping the pairwise closeness. Label semantics are made full use of to avoid classification error. Fourth, we introduce class weight to further increase the discrimination of samples that belong to different classes in intra-modal and keep the similarity of samples unchanged. Therefore, CHRLSG model preserves not only the relationship between samples, but also maintains the consistency of label semantic during collaboration optimization. Experimental results of three common benchmark datasets demonstrate that the proposed model is superior to the existing advanced methods.

有监督的跨模式哈希吸引了许多研究人员。在这些研究中，他们寻求一个共同的语义空间或直接将零一标签信息回归到汉明空间中。尽管他们取得了很多成就，但他们忽略了一些问题：1）分类任务的一些方法不适合检索任务，因为它们缺乏学习样本的个性化特征； 2）哈希检索的结果与哈希码的长度和编码方式有关。由于样本比标签语义具有更多的个性化特征，因此在本文中，我们提出了一种新颖的监督跨模态哈希协作学习方法，称为具有松弛和标签语义指导的离散跨模态哈希（CHRLSG）。首先，我们引入两个松弛变量作为潜在空间。一种用于协同提取文本特征和标注语义信息，另一种用于协同提取图像特征和标注语义。其次，更准确的哈希码是从潜在空间生成的，因为 CHRLSG 以标签为主导、特征为辅助来协作学习特征语义和标签语义。第三，我们利用标签通过保持成对紧密度来加强模间样本的相似关系。充分利用标签语义来避免分类错误。第四，我们引入类别权重，以进一步增加模内对属于不同类别的样本的区分度，并保持样本的相似性不变。因此，CHRLSG模型不仅保留了样本之间的关系，而且在协作优化过程中保持了标签语义的一致性。三个常见基准数据集的实验结果表明，所提出的模型优于现有的先进方法。