The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
On the Euclidean distance of images
Abstract
We present a new Euclidean distance for images, which we call IMage Euclidean Distance (IMED). Unlike the traditional Euclidean distance, IMED takes into account the spatial relationships of pixels. Therefore, it is robust to small perturbation of images. We argue that IMED is the only intuitively reasonable Euclidean distance for images. IMED is then applied to image recognition. The key advantage of this distance measure is that it can be embedded in most image classification techniques such as SVM, LDA, and PCA. The embedding is rather efficient by involving a transformation referred to as Standardizing Transform (ST). We show that ST is a transform domain smoothing. Using the Face Recognition Technology (FERET) database and two state-of-the-art face identification algorithms, we demonstrate a consistent performance improvement of the algorithms embedded with the new metric over their original versions.
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Cites
A Computational Approach to Edge Detection
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Fundamentals of digital image processing
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Huttenlocher, D. P., Klanderman, G. A., & Rucklidge, W. J. (1993) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.232073 · Cited by 4,545
The FERET evaluation methodology for face-recognition algorithms
Phillips, P. J., Moon, H., Rizvi, S. A., & Rauss, P. J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.879790 · Cited by 3,692
Application of the Karhunen-Loeve procedure for the characterization of human faces
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Cited by
Robustness Analysis of Deep Learning-Based Lung Cancer Classification Using Explainable Methods
Malafaia, M., Silva, F., Neves, I., Pereira, T., & Oliveira, H. P. (2022) · IEEE Access · doi:10.1109/access.2022.3214824 · Cited by 25
A Novel Prediction Model for Multimodal Medical Data Based on Graph Neural Networks
Zhang, L., Li, T., Cui, H., Zhang, Q., Jiang, Z., Li, J., Welsch, R. E., & Jia, Z. (2025) · Machine Learning and Knowledge Extraction · doi:10.3390/make7030092 · Cited by 4
Metadata
- Authors
- Wang, L., Zhang, Y., & Feng, J.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2005-08
- Volume
- 27
- Issue
- 8
- Pages
- 1334-1339
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2142431032
- Cited by
- 658
- References
- 17
- FWCI
- 14.36
- Type
- journal article
- Language
- en
- Open access
- closed
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