Dot-Based Image Object Labeling for ML Training
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Solution Overview
Problem
Conventional methods for labeling objects in images, such as drawing polygons to trace object edges, are time-consuming and difficult to use, especially when training image analysis tools like image classifiers.
Innovation Solution
A system and method that allows users to label objects in images by marking sets of dots within or near the object, where the position and number of dots indicate the object's boundary or presence, facilitating faster and more intuitive object identification, and using these labels to train machine learning models for object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users draw polygons to trace object edges for labeling, then labeling precision is improved, but time consumption increases
Solution Approach 1:
The patent segments the complex task of drawing complete polygons into simpler dot placement actions. Users only need to place dots at key locations (corners or center points) rather than tracing entire object boundaries, significantly reducing the time required while maintaining labeling precision through subsequent automated polygon generation from the placed dots.
Solution Approach 2:
The system introduces an intermediary computational process that automatically generates complete polygon labels from user-placed dots. This intermediary step bridges the simple user action of dot placement with the required output format of precise polygon boundaries, eliminating the need for users to manually draw complex shapes while ensuring accurate object boundary definition.
2Measurement precision
If users draw polygons to trace object edges for labeling, then object boundary accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The complex operation of drawing precise polygon boundaries is segmented into simple dot placement actions. Users only need to click on key points (corners or center) of objects, which is much easier to perform than manually tracing object edges with polygons, while the system handles the complex boundary calculation automatically.
Solution Approach 2:
The system performs self-service by automatically generating accurate polygon boundaries from user-placed dots. The computational geometry algorithms automatically calculate precise object boundaries based on the dots provided by users, eliminating the need for users to manually perform the complex task of drawing accurate polygons while maintaining high boundary accuracy.
3Measurement precision
If multiple dots are placed to indicate object position, then object identification accuracy is improved, but device complexity increases
Solution Approach 1:
The system uses partial action by requiring only a few key dots (one or more) to indicate object position rather than requiring complete polygon boundary definition. This partial input is sufficient for the system to generate accurate object identification through automated polygon generation, reducing the complexity of the interaction while maintaining identification accuracy.
Data Source
AI summary
Systems, methods, and non-transitory computer readable media are provided for labeling depictions of objects within images. An image may be obtained. The image may include a depiction of an object. A user's marking of a set of dots within the image may be received. The set of dots may include one or more dots. The set of dots may be positioned within or near the depiction of the object. The depiction of the object within the image may be labeled based on the set of dots.