Label Me - Motivation

Motivation

The motivation behind creating LabelMe comes from the history of publicly available data for computer vision researchers. 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. LabelMe was created to solve several common shortcomings of available data. The following is a list of qualities that distinguish LabelMe from previous work.

• Designed for recognition of a class of objects instead of single instances of an object. For example, a traditional dataset may have contained images of dogs, each of the same size and orientation. In contrast, LabelMe contains images of dogs in multiple angles, sizes, and orientations.
• Designed for recognizing objects embedded in arbitrary scenes instead of images that are cropped, normalized, and/or resized to display a single object.
• 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.
• Contains a large number of object classes and allows the creation of new classes easily.
• Diverse images: LabelMe contains images from many different scenes.
• Provides non-copyrighted images and allows public additions to the annotations. This creates a free environment.