Image Annotation Metadata for Faster Training Data Selection
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Solution Overview
Problem
Current image data processing in computer vision training lacks efficient methods for selecting and preprocessing accurate subject data, leading to training errors and prolonged training times due to manual selection and time-consuming cleaning processes.
Innovation Solution
An image data processing device and method utilizing an annotation algorithm and translation function to annotate features, generate meta-data, and create a data inventory, enabling efficient selection and cleaning of image data based on keywords.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of images is performed, then image data accuracy is improved, but training time is prolonged
Solution Approach 1:
The system enables images to be automatically annotated and selected without human intervention. The annotation algorithm processes images autonomously to generate annotation data, and the translation function automatically selects images based on keywords, replacing manual selection operations.
Solution Approach 2:
The patent replaces manual mechanical selection processes with automated computational systems. The annotation algorithm and translation function use computational methods to process, annotate, and select images, substituting human manual operations with automated digital processing.
2Measurement precision
If image data cleaning is performed manually, then data accuracy is improved, but processing time is increased
Solution Approach 1:
The system performs self-service by automatically annotating images and generating meta-data without human intervention. The annotation algorithm processes images autonomously to extract features and generate accurate annotation data, eliminating the need for manual cleaning operations.
Solution Approach 2:
The patent applies preliminary action by pre-annotating images with annotation data before they are needed for training. The translation function pre-processes images by generating meta-data based on keywords, so that when images are selected for training, they are already prepared and filtered, eliminating the need for time-consuming manual cleaning during the training process.
3Productivity
If automated annotation is used, then processing speed is improved, but annotation accuracy may decrease
Solution Approach 1:
The patent introduces an intermediary translation function that bridges the annotation algorithm and the final meta-data generation. This intermediary component processes the annotation data through keyword-based translation, ensuring that automated processing maintains accuracy by using semantic understanding and contextual analysis rather than simple pattern matching.
Data Source
AI summary
An image data processing device includes a memory and a processor. The processor is configured to execute following steps based on a plurality of instructions of the memory: annotating a plurality of features in an image with corresponding a plurality of annotation data by using an annotation algorithm; and generating a meta-data by using a translation function based on a keyword and the plurality of annotation data; wherein the meta-data is related to the keyword.