Computer Science > Computer Vision and Pattern Recognition
[Submitted on 26 Mar 2019 (v1), last revised 7 Jan 2020 (this version, v3)]
Title:Cross-modal Subspace Learning via Kernel Correlation Maximization and Discriminative Structure Preserving
View PDFAbstract: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.
Submission history
From: Jun Yu [view email][v1] Tue, 26 Mar 2019 11:29:47 UTC (1,711 KB)
[v2] Thu, 10 Oct 2019 04:09:43 UTC (1,371 KB)
[v3] Tue, 7 Jan 2020 03:25:14 UTC (1,420 KB)
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