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Jan 21, 2020 · This paper discusses a method of classifying paintings into two art movements, namely Cubism and Romanticism, using two texture descriptors: ...
This paper discusses a method of classifying paintings into two art movements, namely Cubism and Romanticism, using two texture descriptors: Local Binary ...
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It can be seen that LBP emerges as the best feature for classifying paintings into the Cubism and Romanticism Art Movements using two texture descriptors: ...
Classifying Paintings into Movements using HOG and LBP Features ... To read the full-text of this research, you can request a copy directly from the authors.
Bibliographic details on Classifying Paintings into Movements using HOG and LBP Features.
A new Madhubani art forms database is created from scratch. Classification into different Madhubani styles is attempted. Transfer Learning is utilized.
The aim was to classify painting database into five genres using feature extraction from the images, using each feature individually for classification.
Jan 4, 2018 · HOG is a very good feature descriptor which performs well in object recognition tasks. LBP is an efficient texture feature descriptor. Both ...
We propose a system based on feature extraction and machine learning that is able to understand the scene in the digitized paintings and to classify the art ...
