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Large-Scale Multi-modal Distance Metric Learning with Application to Content-Based Information Retrieval and Image Classification.
- Source :
- International Journal of Pattern Recognition & Artificial Intelligence; Nov2020, Vol. 34 Issue 13, pN.PAG-N.PAG, 18p
- Publication Year :
- 2020
-
Abstract
- Metric learning algorithms aim to make the conceptually related data items closer and keep dissimilar ones at a distance. The most common approach for metric learning on the Mahalanobis method. Despite its success, this method is limited to find a linear projection and also suffer from scalability respecting both the dimensionality and the size of input data. To address these problems, this paper presents a new scalable metric learning algorithm for multi-modal data. Our method learns an optimal metric for any feature set of the multi-modal data in an online fashion. We also combine the learned metrics with a novel Passive/Aggressive (PA)-based algorithm which results in a higher convergence rate compared to the state-of-the-art methods. To address scalability with respect to dimensionality, Dual Random Projection (DRP) is adopted in this paper. The present method is evaluated on some challenging machine vision datasets for image classification and Content-Based Information Retrieval (CBIR) tasks. The experimental results confirm that the proposed method significantly surpasses other state-of-the-art metric learning methods in most of these datasets in terms of both accuracy and efficiency. [ABSTRACT FROM AUTHOR]
- Subjects :
- CONTENT-based image retrieval
DISTANCE education
COMPUTER vision
MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 02180014
- Volume :
- 34
- Issue :
- 13
- Database :
- Complementary Index
- Journal :
- International Journal of Pattern Recognition & Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 147453039
- Full Text :
- https://doi.org/10.1142/S0218001420500342