Publication Type
Journal Article
Version
acceptedVersion
Publication Date
8-2017
Abstract
Learning image similarity plays a critical role in real-world multimedia information retrieval applications, especially in Content-Based Image Retrieval (CBIR) tasks, in which an accurate retrieval of visually similar objects largely relies on an effective image similarity function. Crafting a good similarity function is very challenging because visual contents of images are often represented as feature vectors in high-dimensional spaces, for example, via bag-of-words (BoW) representations, and traditional rigid similarity functions, for example, cosine similarity, are often suboptimal for CBIR tasks. In this article, we address this fundamental problem, that is, learning to optimize image similarity with sparse and high-dimensional representations from large-scale training data, and propose a novel scheme of Sparse Online Learning of Image Similarity (SOLIS). In contrast to many existing image-similarity learning algorithms that are designed to work with low-dimensional data, SOLIS is able to learn image similarity from large-scale image data in sparse and high-dimensional spaces. Our encouraging results showed that the proposed new technique achieves highly competitive accuracy as compared to the state-of-the-art approaches but enjoys significant advantages in computational efficiency, model sparsity, and retrieval scalability, making it more practical for real-world multimedia retrieval applications.
Keywords
Bag-of-words representation, Distance metric, Image Retrieval, Metric learning, Online Learning, Similarity learning
Discipline
Databases and Information Systems | Theory and Algorithms
Research Areas
Data Science and Engineering
Publication
ACM Transactions on Intelligent Systems and Technology
Volume
8
Issue
5
First Page
64: 1
Last Page
22
ISSN
2157-6904
Identifier
10.1145/3065950
Publisher
Association for Computing Machinery (ACM)
Citation
GAO, Xingyu; HOI, Steven C. H.; ZHANG, Yongdong; ZHOU, Jianshe; WAN, Ji; CHEN, Zhenyu; LI, Jintao; and ZHU, Jianke.
Sparse online learning of image similarity. (2017). ACM Transactions on Intelligent Systems and Technology. 8, (5), 64: 1-22.
Available at: https://ink.library.smu.edu.sg/sis_research/3794
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3065950