- Tytuł:
- A hybrid statistical approach for texture images classification based on scale invariant features and mixture gamma distribution
- Autorzy:
-
Benlakhdar, Said
Rziza, Mohammed
Thami, Rachid Oulad Haj - Powiązania:
- https://bibliotekanauki.pl/articles/29520269.pdf
- Data publikacji:
- 2020
- Wydawca:
- Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
- Tematy:
-
statistical image modeling
SIFT
mixture gamma distribution
uniform discrete curvelet transform
classification - Opis:
- Image classification refers to an important process in computer vision. The purpose of this paper is to propose a novel approach named GGD-GMM and based on statistical modeling in wavelet domain to describe textured images and rely on number of principles which give its internal coherence and originality. Firstly, we propose a robust algorithm based on the combination of the wavelet transform and Scale Invariant Feature Transform. Secondly, we implement the aforementioned algorithm and fit the result using the finite mixture gamma distribution (GMM). The results, obtained for two benchmark datasets, show that the proposed algorithm has a good relevance as it provides higher classification accuracy compared to some other well known models see (Kohavi, 1995). Moreover, it shows other advantages relied to Noise-resistant and rotation invariant.
- Źródło:
-
Computer Methods in Materials Science; 2020, 20, 3; 95-106
2720-4081
2720-3948 - Pojawia się w:
- Computer Methods in Materials Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł