Automated Image Item Detection and Quality Assessment for Entity Pages
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Solution Overview
Problem
Online systems face inefficiencies in manually adding tags and identifying high-quality images of items, leading to reduced dissemination of information about entities associated with these items, as the processes are time-consuming and resource-intensive.
Innovation Solution
An online system uses trained models for item detection and quality prediction to automatically identify and assess the quality of images, incorporating content items into entity pages and adding relevant data if the models meet certain probability and quality thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging and quality assessment processes are used, then accuracy of item identification can be maintained, but time consumption and resource requirements increase significantly
Solution Approach 1:
The system pre-trains item detection models and quality prediction models using extensive datasets before deployment. This preliminary action allows the models to be ready for immediate use, eliminating the need for manual tagging and quality assessment during content submission, thus reducing time consumption while maintaining accuracy.
Solution Approach 2:
The patent replaces manual mechanical processes (human reviewers manually tagging items and assessing quality) with automated machine learning models. The item detection model automatically identifies items in images, and the quality prediction model automatically assesses image quality, substituting human effort with computational processes that are faster and scalable.
2Productivity
If automated models are used for item detection and quality prediction, then processing speed and productivity improve, but system complexity increases
Solution Approach 1:
The system is divided into distinct modular components: an item detection model for identifying items in images, a quality prediction model for assessing image quality, and an automated tagging system. Each module operates independently with specific functions, making the complex system manageable and maintainable while achieving high processing speed.
Solution Approach 2:
The patent introduces trained machine learning models as intermediaries between raw image content and the final tagging/quality assessment output. These models serve as mediators that automatically process images and generate structured data, reducing the need for direct human intervention and simplifying the overall workflow despite the underlying complexity of the models.
3Reliability
If manual review of images is performed to identify high-quality images, then quality control can be ensured, but resource requirements and processing time increase
Solution Approach 1:
The quality prediction model is designed to automatically assess image quality without requiring manual review. The model evaluates images based on learned quality metrics and automatically determines which images meet quality thresholds, enabling the system to self-regulate quality control while minimizing human resource requirements.
Solution Approach 2:
The system transforms the quality control process from a manual, subjective evaluation to an automated, parameter-based assessment. The quality prediction model uses quantifiable parameters (such as image resolution, clarity, composition metrics) to objectively evaluate image quality, ensuring consistent quality control while reducing resource consumption compared to manual review processes.
Data Source
AI summary
An online system receives a content item including an image from an online system user. The online system accesses and applies a trained item detection model to predict a probability that a region of interest within the image corresponds to an item associated with an entity based on a set of pixel values associated with the region of interest. If the probability is at least a threshold probability, the online system accesses and applies a trained quality prediction model to predict a measure of quality of the image based on a set of attributes of the image. If the measure of quality is at least a threshold measure of quality, the online system includes the content item in a page associated with the entity maintained in the online system and/or adds a set of data associated with the item and/or the entity to the content item.