Texture Segmentation using Siamese Network and Hierarchical Region Merging
Ryusuke Yamada, Hidenori Ide, Novanto Yudistira, Takio Kurita · 2018
This paper proposes an texture segmentation algorithm. In the proposed texture segmentation algorithm, the feature vectors at each pixel of an input image are extracted by using the deep neural networks such as the deep convolutional network (CNN) or the Siamese Network. Then they are used as input of the hierarchical region merging. Unlike the semantic segmentation such as fully connected network (FCN) or U-Net which are based on the supervised learning, the proposed algorithm can correctly segment the texture regions whose texture is taken from the other types of the texture. The effectiveness of the proposed texture segmentation algorithm is experimentally confirmed by using the famous texture images taken from book by P. Brodatz.