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The process for combining binary masks that represent different categories in semantic segmentation involves the following steps:

1. The binary masks corresponding to each category are generated using a neural network trained for semantic segmentation.

2. The binary masks are combined using a pixel-wise logical OR operation to create a single binary mask that represents all categories.

3. The resultant binary mask is then converted to a label image, where each pixel is assigned a label that represents the category it belongs to.

4. The label image is then visualized to show the segmentation results.

5. Post-processing techniques may be applied to refine the segmentation results, such as smoothing the boundaries or removing small isolated regions.