Next Article in Journal
Generative Model for Skeletal Human Movements Based on Conditional DC-GAN Applied to Pseudo-Images
Previous Article in Journal
Sine Cosine Algorithm Assisted FOPID Controller Design for Interval Systems Using Reduced-Order Modeling Ensuring Stability
Article

Deep Feature Learning with Manifold Embedding for Robust Image Retrieval

by 1 and 2,*
1
College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
2
School of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(12), 318; https://doi.org/10.3390/a13120318
Received: 16 November 2020 / Revised: 27 November 2020 / Accepted: 30 November 2020 / Published: 2 December 2020
Conventionally, the similarity between two images is measured by the easy-calculating Euclidean distance between their corresponding image feature representations for image retrieval. However, this kind of direct similarity measurement ignores the local geometry structure of the intrinsic data manifold, which is not discriminative enough for robust image retrieval. Some works have proposed to tackle this problem by re-ranking with manifold learning. While benefiting better performance, algorithms of this category suffer from non-trivial computational complexity, which is unfavorable for its application to large-scale retrieval tasks. To address the above problems, in this paper, we propose to learn a robust feature embedding with the guidance of manifold relationships. Specifically, the manifold relationship is used to guide the automatic selection of training image pairs. A fine-tuning network with those selected image pairs transfers such manifold relationships into the fine-tuned feature embedding. With the fine-tuned feature embedding, the Euclidean distance can be directly used to measure the pairwise similarity between images, where the manifold structure is implicitly embedded. Thus, we maintain both the efficiency of Euclidean distance-based similarity measurement and the effectiveness of manifold information in the new feature embedding. Extensive experiments on three benchmark datasets demonstrate the robustness of our proposed method, where our approach significantly outperforms the baselines and exceeds or is comparable to the state-of-the-art methods. View Full-Text
Keywords: image retrieval; deep feature learning; similarity measurement; manifold embedding image retrieval; deep feature learning; similarity measurement; manifold embedding
Show Figures

Figure 1

MDPI and ACS Style

Chen, X.; Li, Y. Deep Feature Learning with Manifold Embedding for Robust Image Retrieval. Algorithms 2020, 13, 318. https://doi.org/10.3390/a13120318

AMA Style

Chen X, Li Y. Deep Feature Learning with Manifold Embedding for Robust Image Retrieval. Algorithms. 2020; 13(12):318. https://doi.org/10.3390/a13120318

Chicago/Turabian Style

Chen, Xin, and Ying Li. 2020. "Deep Feature Learning with Manifold Embedding for Robust Image Retrieval" Algorithms 13, no. 12: 318. https://doi.org/10.3390/a13120318

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop