Automated Image Annotation System for Fine-Grained Classification
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Solution Overview
Problem
Current computer vision systems face challenges in achieving accurate fine-grained image classification due to the time-consuming and error-prone process of collecting and labeling large-scale datasets, especially when the existing datasets are not tailored to specific classification tasks.
Innovation Solution
A method and system that utilize a mobile device to rapidly capture and annotate images of product items, generating a large-scale fine-grained image classification dataset by cropping images, detecting objects, associating them with unique identifiers, and transmitting data to a remote computing system for automated labeling, thereby creating a customized training dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation methods are used to create fine-grained image datasets, then annotation accuracy can be maintained, but the time and effort required increases significantly
Solution Approach 1:
The system enables automated self-annotation by having the mobile device automatically capture images, detect objects, extract features, and generate annotations without requiring manual human intervention for each image labeling task
Solution Approach 2:
The patent replaces the manual mechanical process of human annotation with an automated computational system that uses computer vision algorithms, machine learning models, and automated feature extraction to perform annotation tasks
2Quantity of substance
If existing coarse-grained datasets like ImageNet are used for training, then large-scale training data is available, but fine-grained classification accuracy is limited
Solution Approach 1:
The system segments the annotation process into distinct automated components: image capture, object detection, feature extraction, and label generation, allowing each component to be optimized independently for both scale and precision
Solution Approach 2:
The patent changes the parameter of annotation granularity from coarse-grained to fine-grained by implementing automated detection and classification systems that can identify and label specific sub-categories and detailed object properties
3Productivity
If automated annotation methods are used to rapidly create datasets, then productivity increases, but annotation accuracy may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the automated annotation results are evaluated and refined, allowing the system to learn from its own outputs and improve accuracy over time while maintaining high productivity
Solution Approach 2:
The patent performs preliminary actions by pre-training detection models and feature extractors on large datasets before deploying them for fine-grained annotation, ensuring that the automated system starts with high baseline accuracy
Data Source
AI summary
Systems and methods for automating image annotations are provided, such that a large-scale annotated image collection may be efficiently generated for use in machine learning applications. In some aspects, a mobile device may capture image frames, identifying items appearing in the image frames and detect objects in three-dimensional space across those image frames. Cropped images may be created as associated with each item, which may then be correlated to the detected objects. A unique identifier may then be captured that is associated with the detected object, and labels are automatically applied to the cropped images based on data associated with that unique identifier. In some contexts, images of products carried by a retailer may be captured, and item data may be associated with such images based on that retailer's item taxonomy, for later classification of other/future products.