Placeholder Image Identification via Vector and Text Comparison
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Solution Overview
Problem
Existing methods for detecting placeholder images in e-commerce platforms are inaccurate and require complex machine learning models and extensive training data, making them inefficient and scalable challenges.
Innovation Solution
The proposed solution involves using image vectorizing with machine learning models and vector comparison techniques to identify visually similar placeholder images, and employing optical character recognition (OCR) to extract and compare text indicating placeholder status, with feedback loops to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine learning models and extensive training data are used for detecting placeholder images, then detection capability is improved, but system complexity and computational resources increase
Solution Approach 1:
The detection system is divided into multiple specialized modules: an image vectorization module that converts images to vectors, a vector comparison module that compares vectors against a database, an OCR module that extracts text from images, and a text comparison module that checks for placeholder text patterns. This segmentation allows each module to perform a specific function efficiently without requiring a single complex model to handle all aspects of placeholder detection.
Solution Approach 2:
The patent introduces intermediate processing steps between image input and final detection results. Image vectors serve as an intermediary representation that captures visual features without requiring full image analysis. Similarly, extracted text acts as an intermediary that bridges visual content and placeholder identification, enabling more efficient and accurate detection through multiple processing stages.
2Measurement precision
If existing machine learning methods are used for placeholder detection, then detection function is achieved, but accuracy remains insufficient
Solution Approach 1:
The system implements feedback loops where detection results are continuously refined. The vector comparison module compares image vectors against a database of known placeholder vectors, and the text comparison module checks extracted text against known placeholder text patterns. This feedback mechanism allows the system to learn from previous detections and improve accuracy over time, achieving approximately 95% accuracy in identifying placeholder images.
Solution Approach 2:
The patent replaces traditional machine learning model-based detection with a hybrid approach that substitutes certain mechanical processing steps with alternative methods. Instead of relying solely on complex neural network inference, the system uses deterministic vector comparison algorithms and rule-based text pattern matching, which provide more reliable and interpretable results while maintaining high accuracy.
3Productivity
If traditional detection methods are used, then implementation is straightforward, but scalability to large catalogs is limited
Solution Approach 1:
The system performs preliminary processing of images by converting them to vectors and extracting text content before the actual placeholder detection occurs. This preliminary action prepares the data in advance, allowing for rapid comparison against the database during detection. Image vectorization and OCR text extraction are performed upfront, enabling efficient batch processing of large product catalogs without increasing processing complexity during the detection phase.
Solution Approach 2:
The patent creates simplified copies of images in the form of vector representations that capture essential visual features without requiring the full original image data for comparison. These vector copies enable efficient storage and rapid comparison operations, allowing the system to scale to large catalogs while maintaining detection accuracy and reducing computational complexity during the detection process.
Data Source
AI summary
Identification of an input image as a placeholder image has an image comparison part against known placeholder images and a text comparison part. A set of text characters are extracted and are tokenized into constituent input image words of one or more phrases. A placeholder text match score is generated from the input image words evaluated against a placeholder text wordlist of known placeholder phrases. The input image can be flagged as a placeholder based at least partially upon the placeholder text match score.