Image Content Moderation Using Fusion-Based Tagging
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Solution Overview
Problem
Existing image content moderation technologies face challenges in categorizing and tagging objects in images, particularly on e-commerce websites, due to the difficulty in determining appropriate or inappropriate content and accurately classifying products by gender and attributes like color and fabric, especially when products are displayed by individuals whose gender differs from the product's intended gender.
Innovation Solution
The image content moderation apparatus employs a combination of machine learning and deep learning techniques, including convolutional neural networks, to classify objects into categories like male, female, or neutral, and tag products with attributes such as color, using a fusion-based approach that integrates inappropriate content tagging, category classification, and detailed tagging, with features like region-based analysis and blur detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image content moderation methods are used, then the system is simple to implement, but the accuracy in categorizing and tagging objects is insufficient
Solution Approach 1:
The system segments the image content moderation task into multiple specialized components: inappropriate content detection module, object detection module, category classification module, and attribute tagging module. Each module focuses on a specific aspect of analysis, improving overall accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The system merges multiple machine learning models and analysis techniques into a unified content moderation pipeline. By combining inappropriate content detection, object detection, category classification, and attribute tagging in an integrated system, it achieves comprehensive and accurate image analysis that surpasses individual methods.
2Measurement precision
If machine learning and deep learning techniques are employed, then the categorization and tagging accuracy improves, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by first detecting inappropriate content and identifying objects before proceeding to category classification and attribute tagging. This staged approach allows early termination for inappropriate content and focuses computational resources only on relevant regions, reducing overall resource consumption while maintaining high accuracy.
Solution Approach 2:
The system applies local quality by focusing detailed analysis only on detected objects and regions of interest rather than processing the entire image uniformly. The attribute tagging and category classification are applied selectively to identified objects, optimizing computational efficiency while preserving tagging accuracy.
3Loss of information
If comprehensive attribute tagging is implemented, then the product information completeness improves, but the processing complexity increases
Solution Approach 1:
The system segments the attribute tagging process into distinct categories (color, size, material, style) with dedicated processing for each attribute type. This segmentation allows comprehensive information extraction while managing complexity through specialized processing pipelines for each attribute category.
Solution Approach 2:
The system implements a universal object detection and analysis framework that can extract multiple types of attributes (color, size, material, style) from the same detected objects. This multi-functional approach ensures complete product information capture without requiring separate processing systems for each attribute type.
Data Source
AI summary
In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.