Semi-Automated Image Classifier Training via Feature Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image classification systems require extensive manual annotation for training, which is time-consuming and inefficient, especially when identifying objects not present in initial training data or requiring labeling of similar objects as groups.
Innovation Solution
A semi-automated method that utilizes a clustering model to group images with similar features, allowing annotators to label at the group level, thereby reducing human involvement in generating large sets of labeled training data for image classifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used for training image classifiers, then labeling accuracy can be ensured, but the time consumption and labor cost increase significantly
Solution Approach 1:
The system enables self-service annotation by allowing the image classifier to automatically generate labels for training data. The classifier processes images and generates candidate labels, which are then used to create training datasets without requiring extensive manual annotation, thus reducing time consumption while maintaining acceptable labeling quality
Solution Approach 2:
The system changes the parameter of labeling granularity by shifting from individual image-level manual annotation to cluster-level automated labeling. Images are grouped into clusters based on similarity, and labels are applied at the cluster level, significantly reducing the total number of annotations required while maintaining consistency across similar images
2Adaptability or versatility
If extensive manual annotation is performed to cover diverse objects, then classifier versatility improves, but productivity decreases
Solution Approach 1:
The system segments the diverse object space into multiple clusters based on image similarity. Instead of manually annotating every individual image across all object categories, the system divides images into groups and applies labels at the cluster level, enabling the classifier to learn diverse object categories efficiently while maintaining high productivity
Solution Approach 2:
The system performs preliminary clustering and grouping of images before the actual labeling process. By pre-organizing images into similarity-based clusters, the system prepares the data structure that enables efficient batch labeling, reducing the overall time and effort required for comprehensive object category coverage
3Reliability
If traditional training methods are used for new object categories, then model accuracy can be maintained, but the complexity of the training process increases
Solution Approach 1:
The system merges the clustering process with the training data preparation process. By integrating unsupervised clustering with supervised training, the system automatically generates labeled training data for new object categories without requiring separate manual annotation workflows, thus maintaining model accuracy while reducing training process complexity
Data Source
AI summary
Systems and methods are provided for generating labeled image data for improved training of an image classifier, such as a multi-layered machine learning model configured to identify target image objects in image data. When the initially trained classifier is unable to identify a particular object in input image data, such as an object that did not appear in initial training data, feature information determined by the classifier for the given image data may be provided to a clustering model. The clustering model may group image data having similar features into different clusters or groups, which may in turn be labeled at the group level by an annotator. The image data assigned to the different clusters, along with the associated labels, may subsequently be used as training data for training a classifier to identify the labeled objects in images.