# Deep Learning Methods in Image Matting: A Survey
## 2 Datasets  
[PERSON] et al. [40] created a benchmark comprising 27 training images and 8 test images that can be utilized to evaluate the accuracy of online image matting algorithms in terms of alpha matte quality. The images and corresponding trimaps in this dataset have two different resolution sizes, small and large, which can be used for different application scenarios based on specific requirements. This dataset is the initial benchmark that is specifically designed for image matting, which offers a reliable benchmark for assessing the effectiveness of image matting approaches. [PERSON] et al. [9] created a large-scale dataset named Composition-1K, which contains 49,300 training images and 1000 test images. This dataset was created by manually extracting the foreground from the image using image processing software and then compositing it onto a new background image. This is the inaugural large-scale image matting dataset, which furnishes an ample quantum of data for the training of neural networks. [PERSON] et al. [8] created a portrait dataset containing 2000 images. The authors used advanced image matting algorithms to obtain the alpha mattes of each image and then  
Figure 4: The timeline of deep learning-based matting methods is presented with the methods being listed chronologically according to the year they were proposed. Trimap-based methods are denoted by circles, whereas trimap-free methods are denoted by diamonds. (Related methods and corresponding references: DAPM [8], DIM [9], SHM [10], AM [11], AlphaGan [12], DisIM [13], CAM [14], LSM [15], IndexNet [16], VDRN [17], LFM [18], HATT [19], AlphaNet [20], BGM [21], JMNet [22], SSM [23], GCA [24], SIM [25], HRM [26], LFPNet [27], BGMV2 [28], AIM [29], P3M [30], SalientIM [31], IIM [32], MODNet [33], RVM [34], PPMatting [35], GFM [36], DIIM [37], UGIM [38], MatteFormer [39]). constructed the dataset by manually selecting high-quality alpha mattes. The alpha mattes of each image contained detailed information that was necessary for training and validation. However, noise is inevitably introduced into the alpha mattes of this dataset during algorithm execution. [PERSON] et al. [25] produced a semantic dataset with extensive coverage of image matting patterns, taking into account the balance of data across various semantic categories. This balance ensures that images containing foreground objects in each category are almost equal in quantity. [PERSON] et al. [10] created a large-scale and high-quality portrait matting dataset containing 52,511 images. This dataset includes the foregrounds of humans carrying accessories such as mobile phones and handbags. After recruiting volunteers and carefully screening, the researchers obtained 35,311 foregrounds with high-quality alpha mattes. Additionally, they also selected 202 human foregrounds from the Composition-1K dataset [9]. Background images without humans were selected from the COCO dataset [41] and from the Internet, and they were then composited with the foregrounds. [PERSON] et al. [19] created a dataset called Distinctions-646, which is a large-scale matting dataset consisting of 59,600 training images and 1000 test images. This dataset contains a total of 646 foregrounds composited with different backgrounds. [PERSON] et al. [28] created two datasets named VideoMatte240K and PhotoMatte13K, respectively. VideoMatte240K was created by collecting 484 high-definition green screen videos at 4K resolution. Chroma key software was then used to generate 240,709 frames of alpha mattes and foregrounds. The dataset includes people with different outfits and postures. It is noteworthy that this is the initial video matting dataset consisting of consecutive frames, which is distinct from static images and offers immense potential for future research on video and motion information. PhotoMatte13K comprises 13,665 images taken in a studio setting against a green screen background. Although the dataset offers a limited number of people poses, it boasts a high resolution, which allows it to capture finer details.