Automated Image Annotation via Segmentation and Augmentation
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Solution Overview
Problem
The high cost and time-consuming nature of human annotation for training machine learning models, which limits the expansion of machine learning into new fields and inhibits its adoption due to the need for extensive human-annotated data sets.
Innovation Solution
A system that automatically annotates images by generating augmented images through transformations like rotation, distortion, and recoloring, creating segmentation maps, selecting consistent segments, merging them, and classifying each segment, thereby reducing the reliance on human annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human users annotate images to train machine learning models, then the quality and accuracy of training data is improved, but the cost and time required increases significantly
Solution Approach 1:
The system performs preliminary automated annotation using generated augmented images and segmentation maps before human users need to annotate. By pre-processing images with automated segmentation and generating preliminary annotations, the system reduces the subsequent human annotation workload while maintaining high quality standards.
Solution Approach 2:
The system introduces an intermediary automated annotation process between raw images and final training data. The control circuit generates augmented images, creates segmentation maps, and produces preliminary annotations that serve as an intermediate layer, reducing the direct burden on human annotators while preserving annotation quality.
2Measurement precision
If human users annotate images to train machine learning models, then the accuracy of training data is improved, but the cost increases significantly
Solution Approach 1:
The system enables self-service automated annotation by generating its own training data through image augmentation and segmentation. The control circuit autonomously creates augmented images, generates segmentation maps, and produces annotations without requiring human intervention, thereby eliminating the costly human annotation process while maintaining acceptable annotation quality for training purposes.
Solution Approach 2:
The system creates copies of original images through augmentation transformations (rotation, distortion, recoloring) to generate additional training data. These copied and transformed images serve as substitutes for manually annotated images, reducing the need for expensive human annotation while providing sufficient training data variety.
3Measurement precision
If extensive human-annotated data sets are used to train machine learning models, then the model accuracy is improved, but the productivity and speed of model development decreases
Solution Approach 1:
The system performs preliminary automated data generation and annotation before model training begins. By pre-generating augmented images and segmentation maps automatically, the system eliminates the time-consuming human annotation phase, accelerating model development while providing sufficient data for accurate model training.
Solution Approach 2:
The system replaces the mechanical human annotation process with automated computational methods. The control circuit uses algorithms to generate augmented images, create segmentation maps, and produce annotations, substituting human labor with automated systems that operate faster and at lower cost, thereby improving model development productivity.
Data Source
AI summary
In some embodiments, apparatuses and methods are provided herein useful to automatically annotating images. In some embodiments, a system for automatically annotating images comprises a database, wherein the database is configured to store images and annotations for the images and a control circuit, wherein the control circuit is communicatively coupled to the database, and wherein the control circuit is configured to retrieve, from the database, an image, generate, based on the image, a collection of augmented images, generate segmentation maps for each image in the collection of augmented images, wherein each of the segmentation maps include segments, select, based on a threshold, ones of the segments above a threshold, merge the ones of the segments above the threshold to create a segmented image, and generate, for each segment of the segmented image, classifications, wherein an annotation for the image includes the segmented images and the classifications.