Shortest Paths in HSI Space for Color Texture Classification
This work addresses texture classification for image analysis, but it is incremental as it adapts an existing method to a new color space.
The paper tackled color texture classification by proposing shortest paths in the HSI space to extract features, achieving a highest classification accuracy of 96.93% on the Brodatz database.
Color texture representation is an important step in the task of texture classification. Shortest paths was used to extract color texture features from RGB and HSV color spaces. In this paper, we propose to use shortest paths in the HSI space to build a texture representation for classification. In particular, two undirected graphs are used to model the H channel and the S and I channels respectively in order to represent a color texture image. Moreover, the shortest paths is constructed by using four pairs of pixels according to different scales and directions of the texture image. Experimental results on colored Brodatz and USPTex databases reveal that our proposed method is effective, and the highest classification accuracy rate is 96.93% in the Brodatz database.