Image Matting and Trimaps

Trimaps

Trimap is a one-channel map that represents the absolute background, foreground, and unknown regions of an image. It is primarily created through human annotation, although some supervised and unsupervised machine-learning solutions can be used to generate them. However, the resulting quality may not be as good as a human annotation.

Example Trimap

Below is a sample image and a corresponding trimap.

Sample Application

Here is the foreground selection tool interface of the GIMP image editing program.

Challenges

An ideal trimap should contain a minimal number of unknown pixels, with clear distinctions between the foreground and background regions. It has a significant impact on the quality of the final image matting result. However, it can be challenging to create accurate trimaps due to the following reasons:

• The task may not be obvious for users in the image editing interfaces.
• Even if the user is an expert, it can be time-consuming to create a trimap.
• For some applications, trimap or other annotations may not be be feasible. For example, in real-time video processing, it is not possible to create a trimap for each frame.

Benefits

Even though creating trimaps can be challenging, they are essential for high-quality image matting. Here are some benefits of using trimaps:

• It may improve the accuracy of the image matting process.
• Sometimes, the foreground might be subjective. For example, the user may wish to include a human but not the chair behind them. In this case, the trimap can help the algorithm to make a better decision. In other words, it provides a way to guide the algorithm to make the correct decision.

Generating Trimap From Alpha Matte

In training machine learning algorithms, some models are designed to take the trimap as input along with the image. In this cases, it is not necessary to create a trimap manually, because it could be generated from the alpha matte through morphological operations.

This is how it works:

• First, dilate the mask to expand the regions of foreground and background pixels
• Then, erode the mask to shrink the regions of foreground and background pixels
• The resulting image will be the trimap, with the foreground and background pixels represented by white and black pixels, respectively, and the unknown pixels represented by gray pixels

Here is a function that extracts trimap from the alpha channel (Python):

1import numpy as np
2import cv2 as cv
3
4def generate_trimap(alpha):
5   k_size = random.choice(range(2, 5))
6   iterations = np.random.randint(5, 15)
7   kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (k_size, k_size))
8   dilated = cv.dilate(alpha, kernel, iterations=iterations)
9   eroded = cv.erode(alpha, kernel, iterations=iterations)
10   trimap = np.zeros(alpha.shape, dtype=np.uint8)
11   trimap.fill(128)
12
13   trimap[eroded >= 255] = 255
14   trimap[dilated <= 0] = 0
15
16   return trimap

Make Your Life Easier

withoutBG API provides a simple and efficient way to remove the background from your images without the need for trimaps or other annotations. Test it out with our interactive demo below:

+ Upload an Image

or drag and drop an image here

No images at hand? Try one of these