Deep Hashing with Hash Center Update for Efficient Image Retrieval

In this paper, we propose an approach for learning binary hash codes for image retrieval. Canonical Correlation Analysis (CCA) is used to design two loss functions for training a neural network such that the correlation between the two views to CCA is maximized. The first loss, maximizes the correla...

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Bibliographic Details
Published in:arXiv.org 2021-06
Main Authors: Abin Jose, Filbert, Daniel, Rohlfing, Christian, Jens-Rainer Ohm
Format: Article
Language:English
Subjects:
Binary codes
Correlation analysis
Correlation coefficients
Image management
Image retrieval
Lower bounds
Neural networks
Training
Online Access:Get full text
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Summary:In this paper, we propose an approach for learning binary hash codes for image retrieval. Canonical Correlation Analysis (CCA) is used to design two loss functions for training a neural network such that the correlation between the two views to CCA is maximized. The first loss, maximizes the correlation between the hash centers and learned hash codes. The second loss maximizes the correlation between the class labels and classification scores. A novel weighted mean and thresholding based hash center update scheme is proposed for adapting the hash centers in each epoch. The training loss reaches the theoretical lower bound of the proposed loss functions, showing that the correlation coefficients are maximized during training and substantiating the formation of an efficient feature space for image retrieval. The measured mean average precision shows that the proposed approach outperforms other state-of-the-art approaches in both single-labeled and multi-labeled image datasets.
ISSN:2331-8422