Image Annotation System Using Tag Co-occurrence Probability
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Solution Overview
Problem
The existing image annotation processes are largely manual, especially for multi-tag annotations, which require significant manpower and resources, especially for images with many features.
Innovation Solution
An annotation method and device that determines a first probability value for annotating an image with a certain tag based on the annotation of another tag in a sample image set, and adds this probability value to the tag information of another image set to improve annotation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used for multi-tag images, then annotation accuracy can be maintained, but the consumption of manpower and material resources increases drastically
Solution Approach 1:
The patent introduces a computer system as an intermediary between the annotator and the annotation task. The system automatically performs tag propagation by calculating co-occurrence probabilities between tags and applying them to annotate images, reducing manual effort while maintaining annotation quality through algorithmic precision
Solution Approach 2:
The patent replaces the mechanical manual annotation process with an automated computational system. Instead of manually examining and annotating each image, the system uses co-occurrence analysis algorithms to automatically propagate tags across images, substituting human labor with computational processing
2Productivity
If automatic tag propagation is used, then annotation efficiency is improved, but the complexity of the annotation process increases
Solution Approach 1:
The patent performs preliminary co-occurrence analysis to build a probability matrix before actual tag propagation. By pre-calculating the relationships between tags and storing them in a structured matrix, the system simplifies the subsequent annotation process and makes it more manageable despite the underlying complexity
3Measurement precision
If co-occurrence analysis is performed on large image sets, then tag propagation accuracy improves, but the time and computational resources required increase
Solution Approach 1:
The patent segments the large-scale co-occurrence analysis into manageable components by organizing tags into a structured matrix format. This segmentation allows the system to process and store co-occurrence probabilities in an organized manner, making the computation more efficient and scalable for large image sets
Data Source
Figure 1A~1B
Figure 1C
Figure 2
AI summary
An annotation method and device and a storage medium are provided. The annotation method includes operations as follows. A first probability value that a first sample image is annotated with an Nth tag when the first sample image is annotated with an Mth tag is determined based on first tag information of a first image set (S11). M and N are unequal and are positive integers. The first probability value is added to second tag information of a second sample image annotated with the Mth tag in a second image set (S12).