Version 1
: Received: 22 September 2023 / Approved: 25 September 2023 / Online: 26 September 2023 (05:13:58 CEST)
How to cite:
ADIL, R.; Kamel, B.; Amina, B. Deep Supervised Hashing by Fusing Multiscale Deep Features. Preprints2023, 2023091699. https://doi.org/10.20944/preprints202309.1699.v1
ADIL, R.; Kamel, B.; Amina, B. Deep Supervised Hashing by Fusing Multiscale Deep Features. Preprints 2023, 2023091699. https://doi.org/10.20944/preprints202309.1699.v1
ADIL, R.; Kamel, B.; Amina, B. Deep Supervised Hashing by Fusing Multiscale Deep Features. Preprints2023, 2023091699. https://doi.org/10.20944/preprints202309.1699.v1
APA Style
ADIL, R., Kamel, B., & Amina, B. (2023). Deep Supervised Hashing by Fusing Multiscale Deep Features. Preprints. https://doi.org/10.20944/preprints202309.1699.v1
Chicago/Turabian Style
ADIL, R., BELLOULATA Kamel and BELALIA Amina. 2023 "Deep Supervised Hashing by Fusing Multiscale Deep Features" Preprints. https://doi.org/10.20944/preprints202309.1699.v1
Abstract
Deep networks-based hashing has gained significant popularity in recent years, particularly in the field of image retrieval. However, most existing methods only focus on extracting semantic information from the final layer, disregarding valuable structural information that contains important semantic details crucial for effective hash learning. To address this limitation and improve image retrieval accuracy, we propose a novel deep hashing method called Deep Supervised Hashing by Fusing Multiscale Deep Features (DSHFMDF). Our approach involves extracting multiscale features from multiple convolutional layers and fusing them to generate more robust representations for efficient image retrieval. Experimental results on CIFAR10 and NUS-WIDE datasets demonstrate that our method surpasses the performance of state-of-the-art hashing techniques.
Keywords
Image retrieval, Deep learning, Multi-scale feature, Deep supervised hashing .
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
