Gustave is an image annotation and labeling tool developed by Google that is used to collect and label images for machine learning purposes It is based on the open-source opencv computer vision library, which was developed to enable computers to understand the contents of images.
Gustave allows users to quickly annotate objects of interest in images, which can be used to train machine learning models. This is done by manually drawing rectangles and polygons around objects of interest and labeling these objects with relevant class labels. Gustave also enables users to annotate semantic segmentation masks and classify images into different categories. Each annotation is reviewed and refined using human-in-the-loop techniques, before it can be used for machine learning.
Five interesting essay topics related to Gustave include:
1. An Examination of Human-in-the-Loop Techniques in Gustave: This essay would explore the use of human-in-the-loop techniques in the label refinement process of Gustave image annotation.
2. Machine Learning Using Images Annotated with Gustave: This essay would analyze the accuracy and effectiveness of machine learning algorithms when trained with images annotated with Gustave.
3. An Evaluation of the Effectiveness of Gustave for Semantic Segmentation: This essay would analyze the accuracy of semantic segmentation masks generated by Gustave, and evaluate its effectiveness as a tool for this purpose.
4. Automated Image Annotation Using Gustave: This essay would discuss the potential for automated annotation of images using Gustave, discuss its advantages and limitations, and explore possible solutions for overcoming the limitations.
5. An Exploratory Study of Gustave's Opencv Library: This essay would explore the opencv library that powers Gustave and analyze the various algorithms used in the annotation and label refinement process.