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- W4386004577 abstract "The Efficient Manifold Ranking (EMR) is a scalable graph-based ranking algorithm that is applied widely in Content-based Image Retrieval (CBIR). However, the effectiveness of an EMR algorithm depends on (1) the feature extraction technique applied to images to extract feature vectors and (2) the relational graph architecture of anchor points built inside the EMR. To address the first problem, EfficientNet-B7 + is proposed in this article which is fine-tuned from a pre-trained model of EfficientNet and is used to extract deep feature vectors of images. Regarding the second problem, we adopt the relational graph architecture of lvdc-EMR, in which the anchor points of the graph are generated by a variant of the Fuzzy C-Mean (FCM) clustering algorithm that was developed by our research team. The experiments conducted on three benchmark datasets Logo2K +, VGGFACE2-S, and Corel30K bring the mean image retrieval accuracy to 88%, demonstrating the effectiveness of our proposed method. Comparing the average values while retrieving using lvdc-EMR, the proposed EfficientNet-B7 + obtains from 4% to 6% better than the original EfficientNet-B7." @default.
- W4386004577 created "2023-08-20" @default.
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- W4386004577 date "2023-01-01" @default.
- W4386004577 modified "2023-10-12" @default.
- W4386004577 title "Improved EfficientNet Network for Efficient Manifold Ranking-Based Image Retrieval" @default.
- W4386004577 cites W2018398888 @default.
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- W4386004577 doi "https://doi.org/10.1007/978-981-99-4725-6_79" @default.
- W4386004577 hasPublicationYear "2023" @default.
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