Class-Aware Object Marking Tool for ML Data Annotation
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Solution Overview
Problem
Current systems lack effective methods for class-aware object marking in images, particularly in selecting and annotating specific object types within images, which hinders efficient data annotation and utilization in machine learning applications.
Innovation Solution
The development of systems and methods that allow for the creation, maintenance, and usage of datasets and annotations, enabling class-aware object marking by receiving user input to select specific object types within images, and utilizing non-transitory computer-readable storage media to implement these methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object marking methods are used without class awareness, then the process is simpler and faster, but the precision and accuracy of object selection and annotation deteriorates
Solution Approach 1:
The patent segments the object marking process into distinct phases: initial image presentation, class-type selection, object selection within the selected class, and annotation. By dividing the complex task of object marking into manageable segments with specific controls for each phase, the system achieves precise class-aware object selection without overwhelming complexity in the overall system architecture
Solution Approach 2:
The patent implements preliminary action by requiring users to select a class type before selecting specific objects. This pre-selection of classification criteria narrows the search space and guides the subsequent object selection process, improving precision by establishing classification boundaries before detailed object marking occurs
2Manufacturing precision
If class-aware object selection is implemented, then the accuracy of data annotation improves, but the time required for annotation increases
Solution Approach 1:
The patent implements dynamic adaptability by allowing the system to respond to different class selections with appropriate object selection options. The interface dynamically updates based on the selected class type, presenting relevant objects for annotation. This dynamic behavior maintains high annotation accuracy while optimizing the process flow to minimize time loss through context-aware object presentation
3Adaptability or versatility
If multiple object types are available for selection, then the versatility and applicability of the system improves, but the complexity of the selection interface increases
Solution Approach 1:
The patent adds a classification dimension to the object selection interface by organizing objects into class types. Instead of presenting all objects in a single undifferentiated list, the system introduces a hierarchical dimension where users first select a class category, then select specific objects within that class. This dimensional organization maintains versatility across multiple object types while keeping the interface manageable through structured categorization
Data Source
AI summary
Systems and methods for class aware object marking are provided. For example, an indication of a selected object type of a plurality of alternative object types may be received. Further, in some examples, an image may be presented to a user. Further, in some examples, an input to an object selection tool may be received from the user. Further, in some examples, in response to a first selected object type and the input to the object selection tool, a first region of the image corresponding to an object of the first selected object type in the image may be selected, and in response to a second selected object type and the input to the object selection tool, a second region of the image corresponding to an object of the second selected object type in the image may be selected.