Aquatic Life Image Curation for Selective Labeling and Storage
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Solution Overview
Problem
Existing image processing systems for aquatic life data face challenges in efficient data labeling, storage, and privacy compliance, leading to resource wastage and suboptimal performance in machine learning applications.
Innovation Solution
An aquatic life data curation system that implements data annotation and storage rules to selectively label and store images, ensuring compliance with regulations like GDPR, and uses these images to fine-tune machine learning models, filter noise, and prioritize relevant data for downstream tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all aquatic life images are processed for labeling and storage, then data completeness is improved, but computing resources and processing time are wasted
Solution Approach 1:
The system performs preliminary actions by applying annotation rules and storage rules to filter and select images before they are processed for labeling. Images that meet the criteria are selected for annotation, while others are discarded or stored selectively, avoiding unnecessary processing of all images.
Solution Approach 2:
The system extracts only the necessary subset of images that meet the annotation and storage rules. By using the rules to identify and separate relevant images from irrelevant ones, the system processes only the essential data needed for the machine learning task, reducing computational waste.
2Measurement precision
If all aquatic life images are labeled, then training data quality is improved, but processing time and computational cost increase
Solution Approach 1:
The system applies annotation rules as a preliminary filter to determine which images require labeling. This preliminary action ensures that only images meeting the criteria are processed for annotation, reducing the total processing time while maintaining the quality of training data through selective labeling.
Solution Approach 2:
The system uses partial action by labeling only the subset of images that meet the annotation rules rather than all images. This partial labeling approach maintains sufficient training data quality while significantly reducing processing time and computational resources required.
3Reliability
If images are stored to ensure data privacy compliance, then data privacy is improved, but storage resources and system complexity increase
Solution Approach 1:
The system applies storage rules as a preliminary filter to determine which images should be stored. This preliminary action ensures that only images meeting privacy and compliance criteria are stored, reducing storage resource requirements and simplifying the system architecture compared to storing all images.
Solution Approach 2:
The system extracts and stores only the necessary subset of images that meet data privacy requirements. By using storage rules to identify and separate compliant images from non-compliant ones, the system maintains data privacy reliability while reducing storage resources and system complexity.
4Measurement precision
If rare species images are included in fine-tuning, then model performance for rare species is improved, but processing resources increase
Solution Approach 1:
The system applies annotation rules as a preliminary filter to identify and select images of rare species for fine-tuning. This preliminary action ensures that only relevant images meeting the criteria are processed for model fine-tuning, improving rare species performance while reducing unnecessary processing resources.
Solution Approach 2:
The system extracts and processes only the subset of images that depict rare species or meet the fine-tuning criteria. By using the rules to separate rare species images from common ones, the system improves model performance for rare species while minimizing processing resource consumption on irrelevant images.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing aquatic life data, e.g., aquatic life images. One of the methods includes obtaining aquatic life data; identifying a first subset of the aquatic life data that is to be annotated by one or more data annotators to generate annotated aquatic life data and a second subset of the aquatic life data that is not to be annotated; providing the first subset of the aquatic life data to the one or more data annotators, the one or more data annotators processing the first subset of the aquatic life data to generate the annotated aquatic life data; providing the second subset of the aquatic life data to a data storage curator; and determining whether and, if so, which storage device in one or more storage devices to store the second subset of the aquatic life data.