Reference-Based Image Annotation with SVD and ROI Optimization
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Solution Overview
Problem
Existing image annotation methods require significant time and effort for generating labeled training data, and errors in this data can lead to insufficient accuracy in machine learning models, particularly for supervised learning algorithms.
Innovation Solution
An automatic image annotation system using singular value decomposition (SVD) and pre-trained deep learning models generates vector representations of reference and query images, employing expectation maximization (EM) to optimize annotations, enabling efficient and accurate annotation of regions of interest (ROI) in query images based on a single reference image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual image annotation is performed to generate labeled training data, then annotation accuracy can be ensured, but significant time and effort are required
Solution Approach 1:
The patent uses a reference image with known annotations as a template to generate annotations for query images. The system copies the annotation pattern from the reference image and applies it to similar query images through image matching, thereby automating the annotation process while maintaining consistency and accuracy without manual labeling of each image
Solution Approach 2:
The system enables self-service annotation by automatically generating annotations for query images using the reference image and image matching algorithms. The annotation process does not require manual intervention for each image, as the system autonomously identifies similar regions and transfers annotations, significantly reducing the time and effort required while maintaining accuracy
2Reliability
If large volumes of training data are generated manually, then model training accuracy can be improved, but the cost and time investment increase significantly
Solution Approach 1:
The patent enables rapid generation of large volumes of training data by copying annotations from a single reference image to multiple query images. This approach allows the system to produce extensive labeled datasets efficiently, providing sufficient training data for improving model accuracy without the prohibitive cost and time of manual annotation of each image
Solution Approach 2:
The system performs preliminary action by creating a reference image with accurate annotations beforehand. This pre-annotated reference image serves as a template that can be rapidly applied to generate large volumes of additional training data through automated image matching and annotation transfer, thereby efficiently scaling data generation to improve model training
3Reliability
If errors in annotated data are reduced through manual verification, then model accuracy improves, but the time and resource requirements increase
Solution Approach 1:
The patent reduces errors in annotated data by copying proven accurate annotations from a manually verified reference image to query images. This copying mechanism ensures that only verified, high-quality annotations are transferred, maintaining data quality and reliability without requiring time-consuming manual verification of each individual query image annotation
Data Source
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AI summary
An automatic image annotation system receives a reference image with one or more parts annotated along with one or more query images and automatically identifies portions from the query images that are similar to the annotated parts of the reference image. The S-matrices of the reference image and the query images are obtained via singular value decomposition (SVD). Lower-dimensional images are also obtained for the reference image and the query images using a pre-trained deep learning model. The S-matrices and the lower-dimensional images of the corresponding images are combined to generate vector representations. A distance metric is calculated for the vector representation of the reference image with that of the query image. A preliminary output image with a preliminary annotation is initially generated. The preliminary annotation is further optimized to generate an optimized annotation that adequately covers the region of interest (ROI).