The creators of LabelMe decided to leave these decisions up to the annotator. Should the label be person, man, or pedestrian? The user chooses what text to enter as the label for the object.Should the fingers of a hand on a person be outlined with detail? How much precision must be used when outlining objects? The user has to describe the shape of the object themselves by outlining a polygon.Should an occluded person be labeled? Should an occluded part of an object be included when outlining the object? Should the sky be labeled? The user can choose which objects in the scene to outline.Other problems are caused by the amount of freedom given to the users of the annotation tool. However, cropping and rescaling the images randomly can simulate a uniform distribution. This is due to the images being primarily taken by humans who tend to focus the camera on interesting objects in a scene. Some are inherent in the data, such as the objects in the images not being uniformly distributed with respect to size and image location. Once the user is finished with an image, the Show me another image link can be clicked and another random image will be selected to display to the user. In this way, the data is always changing due to contributions by the community of users who use the tool. If the user disagrees with the previous labeling of the image, the user can click on the outline polygon of an object and either delete the polygon completely or edit the text label to give it a new name.Īs soon as changes are made to the image by the user, they are saved and openly available for anyone to download from the LabelMe dataset. The user can choose whatever label the user thinks best describes the object. Once the polygon is closed, a bubble pops up on the screen which allows the user to enter a label for the object. For example, in the adjacent image, if a person was standing in front of the building, the user could click on a point on the border of the person, and continue clicking along the outside edge until returning to the starting point. If the image is not completely labeled, the user can use the mouse to draw a polygon containing an object in the image. Each distinct object label is displayed in a different color. If the image already has object labels associated with it, they will be overlaid on top of the image in polygon format. When the tool is loaded, it chooses a random image from the LabelMe dataset and displays it on the screen. To access the tool, users must have a compatible web browser with JavaScript support. The tool can be accessed anonymously or by logging into a free account. The LabelMe annotation tool provides a means for users to contribute to the project. Provides non- copyrighted images and allows public additions to the annotations.Diverse images: LabelMe contains images from many different scenes.Contains a large number of object classes and allows the creation of new classes easily.Complex annotation: Instead of labeling an entire image (which also limits each image to containing a single object), LabelMe allows annotation of multiple objects within an image by specifying a polygon bounding box that contains the object.Designed for recognizing objects embedded in arbitrary scenes instead of images that are cropped, normalized, and/or resized to display a single object.In contrast, LabelMe contains images of dogs in multiple angles, sizes, and orientations. For example, a traditional dataset may have contained images of dogs, each of the same size and orientation. Designed for recognition of a class of objects instead of single instances of an object.The following is a list of qualities that distinguish LabelMe from previous work. LabelMe was created to solve several common shortcomings of available data. Most available data was tailored to a specific research group's problems and caused new researchers to have to collect additional data to solve their own problems. The motivation behind creating LabelMe comes from the history of publicly available data for computer vision researchers. As of October 31, 2010, LabelMe has 187,240 images, 62,197 annotated images, and 658,992 labeled objects. The most applicable use of LabelMe is in computer vision research. The dataset is dynamic, free to use, and open to public contribution. LabelMe is a project created by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) which provides a dataset of digital images with annotations. JSTOR ( August 2018) ( Learn how and when to remove this template message).Please improve this article by adding secondary or tertiary sources. This article relies excessively on references to primary sources.
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