Mask Editor : an Image Annotation Tool for Image Segmentation Tasks
This tool addresses a practical problem for researchers and practitioners in computer vision who need to generate image masks for training deep convolutional neural networks, but it is incremental as it builds on existing annotation software concepts.
The authors tackled the inefficiency of existing image annotation tools for marking irregularly shaped objects in image segmentation tasks by developing Mask Editor, which allows drawing any bounding curve and supports various features, resulting in improved annotation efficiency.
Deep convolutional neural network (DCNN) is the state-of-the-art method for image segmentation, which is one of key challenging computer vision tasks. However, DCNN requires a lot of training images with corresponding image masks to get a good segmentation result. Image annotation software which is easy to use and allows fast image mask generation is in great demand. To the best of our knowledge, all existing image annotation software support only drawing bounding polygons, bounding boxes, or bounding ellipses to mark target objects. These existing software are inefficient when targeting objects that have irregular shapes (e.g., defects in fabric images or tire images). In this paper we design an easy-to-use image annotation software called Mask Editor for image mask generation. Mask Editor allows drawing any bounding curve to mark objects and improves efficiency to mark objects with irregular shapes. Mask Editor also supports drawing bounding polygons, drawing bounding boxes, drawing bounding ellipses, painting, erasing, super-pixel-marking, image cropping, multi-class masks, mask loading, and mask modifying.