Object extraction from image with big size based on bilateral grid. (January 2023) - Li, Xiaoli; Du, Weiguo; Chen, Dong
Abstract: Currently image with big size segmentation suffers from low efficiency and inaccurate results, in this paper a novel object extraction approach from image with big size based on the application of a bilateral grid is proposed. Firstly, the bilateral grid is constructed according to the spatial ratio and color sampling ratios, which are determined by both image size and color range. Then, the big image data is splatted into the bilateral grid, the sampled data among the grid vertices are assigned by the nearest vertex data, and all grid vertices are assigned the object label via random forest classification. Finally, the segmentation is reconstructed from the grid data by interpolation. Experiment results show that the proposed algorithm could effectively improve the big image segmentation efficiency, and achieve the better segmentation results than state-of-the-art methods. Highlights: The investigation on the object extraction from the big image is worthy. An approach of object extraction from big image based on bilateral grid is proposed. All grid vertices are assigned the object label via random forest classification. The presented approach measures the two nodes via Jensen–Shannon divergence.