语义相似性保持的判别式跨模态哈希

针对跨模态哈希检索方法中存在标签语义利用不充分,从而导致哈希码判别能力弱、检索精度低的问题,提出了一种语义相似性保持的判别式跨模态哈希方法.该方法将异构模态的特征数据投影到一个公共子空间,并结合多标签核判别分析方法将标签语义中的判别信息和潜在关联嵌入到公共子空间中;通过最小化公共子空间与哈希码之间的量化误差提高哈希码的判别能力;此外,利用标签构建语义相似性矩阵,并将语义相似性保留到所学的哈希码中,进一步提升哈希码的检索精度.在LabelMe、MIRFlickr-25k、NUS-WIDE三个基准数据集上进行了大量实验,其结果验证了该方法的有效性.

Discriminant cross-modal hashing with semantic similarity preservation

In order to solve the problem that the label semantic wasn''t fully utilized in cross modal hash retrieval method, which led to weak discrimination and low retrieval accuracy of hash code, this paper proposed a hash method named DICH-SSP(discriminant cross-modal hash with semantic similarity preservation). It projected the feature of heterogeneous modalities into a common subspace, and embedded the discriminant information and latent correlation of labels into the common subspace combined with multi-label kernel discriminant analysis. Then, it minimized the quantization error between the common subspace and the hash code to improve the discrimination of the hash code. In addition, this paper constructed the semantic similarity matrix by calculating the similarity between labels. DICH-SSP preserved the semantic similarity into hash codes, which could further improve the retrieval accuracy of hash codes. Extensive experiments on the three benchmark data sets of LabelMe, MIRFlickr-25K, and NUS-WIDE demonstrate the effectiveness of DICH-SSP.