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Semantic class discriminant projection for image retrieval with relevance feedback

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Abstract

With the user’s feedback, projections are often used to reduce dimension and enhance class discrimination. The existing projections either use only the global euclidean structure or refer to the local manifold structure. However, global statistics such as variance (ie the method using the global euclidean structure) is difficult to estimate when there are not enough training samples. As for the methods that use the local manifold structure, the class discriminant is limited. In this paper, a Semantic Class Discriminant Projection (SCDP) is proposed for enhancing the performance of content-based image retrieval schemas with relevance feedback. SCDP can take advantage of the local geometry information of labeled and unlabeled images to learn a semantic subspace, and it obtains the most important properties of the subspaces to enhance classification. The experimental results performed on the two benchmark datasets have confirmed the superiority of the proposed method.

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Acknowledgments

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.01-2020.10”.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Thuyloi University, 175 Tayson, Hanoi, Vietnam

    Quynh Nguyen Huu & Dung Cu Viet

  2. Faculty of Information Technology, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam

    Quynh Dao Thi Thuy

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  1. Quynh Nguyen Huu
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  2. Dung Cu Viet
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  3. Quynh Dao Thi Thuy
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Correspondence to Quynh Dao Thi Thuy.

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Huu, Q.N., Viet, D.C. & Thuy, Q.D.T. Semantic class discriminant projection for image retrieval with relevance feedback. Multimed Tools Appl 80, 15351–15376 (2021). https://doi.org/10.1007/s11042-020-10400-y

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  • DOI: https://doi.org/10.1007/s11042-020-10400-y

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