Online Image Search Verification Using Neural Metadata Feedback
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Solution Overview
Problem
Conventional reverse image search engines rely on metadata that can be inaccurate or missing, leading to irrelevant search results due to manual entry and lack of manual verification, and limited object analysis.
Innovation Solution
A neural network-based system that analyzes image-based search queries to determine object types and characteristics, generates unique numeric sequences, and receives user feedback to train the network, providing accurate search results through community verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If metadata is manually entered by content creators, then creator information and keywords can be captured, but the process is tedious, time-consuming and consumes excessive resources
Solution Approach 1:
The patent replaces manual mechanical entry of metadata with automated optical character recognition (OCR) technology and neural networks. The system automatically extracts text, objects, and characteristics from images using computer vision algorithms, eliminating the need for manual typing while maintaining high accuracy in metadata generation.
Solution Approach 2:
The system enables images to self-generate metadata through automated analysis. The neural network independently extracts relevant information such as object types, characteristics, and descriptions from the image content without requiring human intervention, allowing the system to serve itself in the metadata generation process.
2Productivity
If metadata is automatically generated by hardware or software, then time consumption is reduced, but the metadata may be inaccurate or missing when images are copied or modified
Solution Approach 1:
The patent implements a feedback mechanism where community users verify and correct automatically generated metadata. The system presents generated metadata to users for validation, and their corrections are fed back to improve the neural network's future performance. This closed-loop feedback ensures both speed and accuracy in metadata generation.
Solution Approach 2:
The system performs preliminary automated metadata generation before user verification. The neural network pre-processes images and generates initial metadata, which then undergoes community validation. This preliminary action maintains high productivity while the subsequent verification step ensures reliability.
3Measurement precision
If community users verify search results, then metadata accuracy is improved, but the process requires additional user involvement and system complexity
Solution Approach 1:
The patent introduces an intermediary layer between automated metadata generation and final search results. Community users act as intermediaries who verify and refine the automatically generated metadata. This intermediary step improves accuracy while the system manages the complexity through structured verification workflows and incentive mechanisms.
Data Source
AI summary
A system, apparatus, method, and non-transitory computer readable medium for performing image search verification using an online platform may include a memory storing computer readable instructions and a database corresponding to a neural network associated with the online platform, and processing circuitry configured to execute the computer readable instructions. The processing circuitry may cause the server to receive an image-based search query from a first user device, the image-based search query including at least one image including a search object, and search query parameters related to the search object, analyze the search object using the neural network to determine an object type of the search object and at least one object type specific characteristic of the search object, and receive at least one search result response from at least one second user device.