Image Analysis System Using Text-Image Descriptor Matching
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Solution Overview
Problem
Current image recognition algorithms lack specificity in identifying and differentiating between detailed content within images, such as distinguishing between a man and a woman or accurately identifying product brands, which is crucial for applications like contextual advertising and image-indexed searches.
Innovation Solution
The method involves analyzing images with an image recognition engine to obtain image descriptors and simultaneously analyzing proximate text to generate textual descriptors, which are then matched to provide contextual information with higher specificity and confidence, using a computer-based system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current image recognition algorithms are used, then image analysis can be performed, but the specificity in identifying detailed content (such as distinguishing between a man and a woman or accurately identifying product brands) is insufficient
Solution Approach 1:
The patent combines image recognition results with text analysis results to create a hybrid identification system. The image recognition engine generates initial descriptors, which are then refined by matching against textual descriptors extracted from proximate text, thereby merging two different information sources to achieve higher specificity than either method could achieve alone.
Solution Approach 2:
The patent introduces text descriptors as an intermediary element that mediates between the image data and the final identification result. The text descriptors serve as a bridge that provides additional contextual information to disambiguate image recognition results, allowing the system to achieve higher precision by incorporating this intermediate layer of information.
2Reliability
If image recognition algorithms are used alone, then processing speed is maintained, but the accuracy and confidence in identifying specific content is reduced
Solution Approach 1:
The patent segments the identification process into distinct modules: an image recognition engine that processes visual data, a text analysis component that processes proximate text, and a matching engine that correlates results from both. This segmentation allows each component to specialize in its strength while working together to achieve higher overall reliability.
Solution Approach 2:
The system creates a multi-functional identification platform that can handle both pure image recognition tasks and enhanced identification tasks by incorporating text analysis. The same system architecture can operate in different modes depending on whether text analysis is available, providing universal applicability across different scenarios.
3Measurement precision
If text analysis is added to image recognition, then contextual information and specificity are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary text analysis to extract descriptors before matching them with image recognition results. By preparing the text descriptors in advance and organizing them for efficient matching, the system reduces the computational burden during the final correlation step, thereby minimizing additional processing time despite the added text analysis component.
Data Source
AI summary
Provided herein are systems and method for obtaining contextual information of an image published on a digital medium. The methods and systems disclosed herein generally identify and analyze the image to obtain image descriptors corresponding to the image. The methods also identify and analyze text published proximate to the image to obtain textual descriptors, which function to describe, identify, index, or name the image or content within the image. The textual descriptors are then matched to the image descriptors to provide contextual information of the published image.