E-viri
Recenzirano
Odprti dostop
-
Shang, Fanhua; Cheng, James; Liu, Yuanyuan; Luo, Zhi-Quan; Lin, Zhouchen
IEEE transactions on pattern analysis and machine intelligence, 09/2018, Letnik: 40, Številka: 9Journal Article
The heavy-tailed distributions of corrupted outliers and singular values of all channels in low-level vision have proven effective priors for many applications such as background modeling, photometric stereo and image alignment. And they can be well modeled by a hyper-Laplacian. However, the use of such distributions generally leads to challenging non-convex, non-smooth and non-Lipschitz problems, and makes existing algorithms very slow for large-scale applications. Together with the analytic solutions to <inline-formula><tex-math notation="LaTeX">\ell _{p}</tex-math> <inline-graphic xlink:href="shang-ieq1-2748590.gif"/> </inline-formula>-norm minimization with two specific values of <inline-formula><tex-math notation="LaTeX">p</tex-math> <inline-graphic xlink:href="shang-ieq2-2748590.gif"/> </inline-formula>, i.e., <inline-formula> <tex-math notation="LaTeX">p=1/2</tex-math> <inline-graphic xlink:href="shang-ieq3-2748590.gif"/> </inline-formula> and <inline-formula><tex-math notation="LaTeX">p=2/3</tex-math> <inline-graphic xlink:href="shang-ieq4-2748590.gif"/> </inline-formula>, we propose two novel bilinear factor matrix norm minimization models for robust principal component analysis. We first define the double nuclear norm and Frobenius/nuclear hybrid norm penalties, and then prove that they are in essence the Schatten-<inline-formula> <tex-math notation="LaTeX">1/2</tex-math> <inline-graphic xlink:href="shang-ieq5-2748590.gif"/> </inline-formula> and <inline-formula><tex-math notation="LaTeX">2/3</tex-math> <inline-graphic xlink:href="shang-ieq6-2748590.gif"/> </inline-formula> quasi-norms, respectively, which lead to much more tractable and scalable Lipschitz optimization problems. Our experimental analysis shows that both our methods yield more accurate solutions than original Schatten quasi-norm minimization, even when the number of observations is very limited. Finally, we apply our penalties to various low-level vision problems, e.g., text removal, moving object detection, image alignment and inpainting, and show that our methods usually outperform the state-of-the-art methods.
![loading ... loading ...](themes/default/img/ajax-loading.gif)
Vnos na polico
Trajna povezava
- URL:
Faktor vpliva
Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Baze podatkov, v katerih je revija indeksirana
Ime baze podatkov | Področje | Leto |
---|
Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
---|
Vir: Osebne bibliografije
in: SICRIS
To gradivo vam je dostopno v celotnem besedilu. Če kljub temu želite naročiti gradivo, kliknite gumb Nadaljuj.