Title
Optimized feature space learning for generating efficient binary codes for image retrieval
Abstract
In this paper, a novel approach for learning a low-dimensional optimized feature space for image retrieval with minimum intra-class variance and maximum inter-class variance is proposed. The classical approach of Linear Discriminant Analysis (LDA) is generally used for generating an optimized low-dimensional feature space for single-labeled images. Since image retrieval involves images with multiple objects, LDA cannot be directly used for dimensionality reduction and feature space optimization. This problem is addressed by utilizing the relationship between LDA and Canonical Correlation Analysis (CCA) eigenvalues to generate an optimized feature space for both single-labeled and multi-labeled images. A CCA-based network architecture which correlates the low-dimensional feature vectors with the image label vectors is proposed. We design a novel loss function such that the correlation coefficients of CCA are maximized. Our experiments prove that we could train the neural network to reach the theoretical lower bound of loss corresponding to the negative sum of the correlation coefficients. Once the optimized feature space is generated, feature vectors are binarized with the Iterative Quantization (ITQ) approach. Finally, we propose an ensemble network to generate binary codes of desired bit length for retrieval. The measurement of mean average precision shows that the proposed approach outperforms the retrieval results of other single-labeled and multi-labeled image retrieval benchmarks at same bit numbers in a considerable number of cases.
Year
DOI
Venue
2022
10.1016/j.image.2021.116529
Signal Processing: Image Communication
Keywords
DocType
Volume
Linear Discriminant Analysis,Canonical Correlation Analysis,Hashing,Image retrieval,Iterative Quantization
Journal
100
ISSN
Citations 
PageRank 
0923-5965
0
0.34
References 
Authors
2
4
Name
Order
Citations
PageRank
abin jose112.40
Ottlik Erik Stefan200.34
Christian Rohlfing302.37
Ohm Jens-Rainer400.34