Image Recognition System for Remote Content Search
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Solution Overview
Problem
Current digital image processing technologies lack effective methods for programmatically detecting and analyzing objects within images, especially in e-commerce contexts, where visual search capabilities are limited to text-based metadata, failing to efficiently retrieve and organize images based on visual content.
Innovation Solution
A system that includes modules for image analysis, segmentation, feature extraction, and manual enrichment, enabling the detection and classification of objects within images through programmatic analysis and human validation, allowing for visual and textual search capabilities within e-commerce applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text-based metadata search is used for image retrieval, then the search system is simple to implement, but the search accuracy and relevance are insufficient
Solution Approach 1:
The patent introduces an image analysis module as an intermediary between the user query and the image database. This module automatically extracts visual features (color, shape, texture) from images and generates structured metadata, serving as a bridge that transforms raw image data into searchable information without requiring complex manual tagging by users
Solution Approach 2:
The system performs preliminary image analysis and feature extraction during the indexing phase, before actual search operations. By pre-processing images to extract and store visual features in advance, the system enables fast and accurate visual searches without performing complex analysis during the search itself
2Productivity
If automated image analysis is implemented, then visual search capability is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs image analysis, feature extraction, and metadata generation during the indexing phase, before actual search operations. By pre-processing images to extract and store visual features in advance, the system enables fast and accurate visual searches without performing complex analysis during the search itself
Solution Approach 2:
The image analysis process is divided into separate modules: color analysis, shape analysis, texture analysis, and object recognition. Each module processes specific visual features independently, allowing parallel processing and optimizing the use of computational resources to reduce overall processing time
3Measurement precision
If comprehensive image analysis is performed, then object detection accuracy is improved, but the complexity of analysis increases
Solution Approach 1:
The image analysis process is divided into separate modules: color analysis, shape analysis, texture analysis, and object recognition. Each module processes specific visual features independently, allowing parallel processing and optimizing the use of computational resources to reduce overall processing time
Solution Approach 2:
The image analysis module is designed to perform multiple functions simultaneously: extracting color information, analyzing shapes, identifying textures, and recognizing objects. This multi-functional approach consolidates what would otherwise require separate processing systems into a single integrated module
Data Source
AI summary
Images are analyzed by programmatic mechanisms for assessing one or more remote web pages to retrieve content on display at remote web pages. The retrieved images may be analyzed to determine information about an object shown in a corresponding images of the content on display. At least a portion of the object shown in the corresponding image of the content on display may be made selectable and associated with the determined information. This determined information may subsequently be used, in for example, search applications.