arXiv Open Access 2010

On the Subspace of Image Gradient Orientations

Georgios Tzimiropoulos Stefanos Zafeiriou

Lihat Sumber

Abstrak

We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data population. We show that replacing intensities with gradient orientations and the $\ell_2$ norm with a cosine-based distance measure offers, to some extend, a remedy to this problem. Our scheme requires the eigen-decomposition of a covariance matrix and is as computationally efficient as standard $\ell_2$ PCA. We demonstrate some of its favorable properties on robust subspace estimation.

Topik & Kata Kunci

cs.CV

Penulis (2)

Georgios Tzimiropoulos

Stefanos Zafeiriou

Format Sitasi

APA MLA BibTeX

Tzimiropoulos, G., Zafeiriou, S. (2010). On the Subspace of Image Gradient Orientations. https://arxiv.org/abs/1005.2715

Akses Cepat

Lihat di Sumber

Informasi Jurnal

Tahun Terbit: 2010
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓