# Interactive Part Segmentation Using Edge Images
## 1 Introduction  
There are many results of using convolutional neural networks (CNNs) in research fields. In various industrial fields, there have been many attempts to apply the research results obtained through CNN models to specific applications. To properly apply the results obtained using a CNN model to specific applications, training data suitable for the application is required. One of the industries that often use images is augmented reality. Augmented reality provides useful information to users based on recognized real space or objects. For example, trainees learning to assemble a car can learn how to assemble it based on car parts recognized by an augmented reality device such as an HMD or smartphone. As another example, trainees learning machine maintenance can easily recognize the location or condition of a part with an augmented reality device and learn behaviors appropriate to the current machine condition to repair the machine. To train a CNN model suitable for a specific application in augmented reality, training data suitable for a specific scenario is required. For example, to train a CNN model for recognizing the class of an object, the class labels corresponding to images are required. To train a CNN model for recognizing which part an object is composed of, the mask labels indicating the location and area of each part of the object and the class name of each part are required. It takes a large amount of time and manpower to make the mask labels from the suitable dataset for a specific application. Since each object is usually composed of several parts, making the mask labels of each part takes longer than making the mask labels of each object. Interactive object segmentation methods enable a user to segment the area pixel level of a target object area through user interaction. Methods using CNNs have emerged that improve the accuracy of object segmentation over classical algorithm methods (e.g., GrabCut [1]). The interactive object segmentation methods using CNN easily and accurately make the mask labels. Interactive object segmentation models based on CNN are generally supervised learning through images and ground truth mask labels in the dataset. Due to the limited number of classes of objects in the dataset, the models utilize the backbone of classification models trained with Image Net datasets, which contain the largest number of object classes. However, it is difficult to accurately segment the target part in an object with the models for segmenting the target object. One of the reasons is that the size of the target part is smaller than the size of the target object. Another reason for the lower accuracy of the target part is that the texture in the interaction area during part segmentation is different from the texture during object segmentation. For example, when a user segments the person in the image with the interactive object segmentation model and interaction, it can accurately segment the target object (e.g., person) because it is trained using the clicked areas which contain the different parts of the object. However, when a user segments the target part (e.g., nose) with the same model, it cannot accurately segment the target partly because the model has never been trained with the texture around the nose that the user clicked on. Table 1 below shows the estimated masks when the interactive object segmentation models segment the motorcycle (object) and the motorcycle's wheel (part). For the two models tested, the accuracy of part segmentation is much lower than the accuracy of object segmentation. The edge image can be expressed as a binary image if the pixel is greater than the threshold and the pixel is true or false, otherwise, it be expressed as a grayscale image according to the value of the change in the local area. The size of the dataset which consists of edge images is lower than the size of the color image dataset because the edge image consists of a single channel, whereas the color image consists of three or four channels.