Cross-modal Subspace Learning via Kernel Correlation Maximization and Discriminative Structure Preserving
This work addresses the challenge of cross-modal similarity measurement for applications like multimedia retrieval, though it appears incremental as it builds on existing subspace learning methods.
The paper tackles the problem of measuring similarity between heterogeneous data by proposing a cross-modal subspace learning framework that preserves semantic structure and ensures consistency between feature and semantic similarity, achieving competitive results on three public datasets.
The measure between heterogeneous data is still an open problem. Many research works have been developed to learn a common subspace where the similarity between different modalities can be calculated directly. However, most of existing works focus on learning a latent subspace but the semantically structural information is not well preserved. Thus, these approaches cannot get desired results. In this paper, we propose a novel framework, termed Cross-modal subspace learning via Kernel correlation maximization and Discriminative structure-preserving (CKD), to solve this problem in two aspects. Firstly, we construct a shared semantic graph to make each modality data preserve the neighbor relationship semantically. Secondly, we introduce the Hilbert-Schmidt Independence Criteria (HSIC) to ensure the consistency between feature-similarity and semantic-similarity of samples. Our model not only considers the inter-modality correlation by maximizing the kernel correlation but also preserves the semantically structural information within each modality. The extensive experiments are performed to evaluate the proposed framework on the three public datasets. The experimental results demonstrated that the proposed CKD is competitive compared with the classic subspace learning methods.