Neural Network Image Search Query Interpretation
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Solution Overview
Problem
Conventional image searching systems are inefficient in understanding vague or critique-based search queries, leading to undesirable search results and requiring users to input multiple queries to refine their searches, which can be time-consuming and difficult to track.
Innovation Solution
A neural network-based machine learning model is used to convert user inputs into preference statements, which are then combined with encoded reference images to generate multi-modal search encodings, allowing for the retrieval of images that differ based on user preferences, thereby overcoming the limitations of keyword-based and natural language constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional keyword-based search systems are used, then search speed is fast, but the system cannot understand vague or critique-based queries leading to undesirable results
Solution Approach 1:
The patent introduces an intermediary natural language processing layer between the user's vague query and the image database. This intermediary translates critique-based queries into structured search parameters, enabling the system to understand intent without requiring complex re-engineering of the entire search infrastructure.
Solution Approach 2:
The patent replaces traditional keyword-matching mechanical systems with neural network-based semantic understanding. The neural networks analyze the meaning and intent behind queries rather than relying on exact keyword matches, significantly improving understanding of vague or critique-based searches.
2Measurement precision
If users input multiple independent queries to refine searches, then search result accuracy improves, but time consumption increases and query relationships become difficult to track
Solution Approach 1:
The patent implements feedback mechanisms where the system analyzes user interactions with search results and automatically refines subsequent queries. The neural networks learn from user preferences and behavior patterns, progressively improving search accuracy without requiring users to manually input multiple refinement queries.
Solution Approach 2:
The system performs preliminary analysis of user intent and potential refinement directions before the user completes their search. By anticipating needed refinements and preparing structured query variations in advance, the system reduces the time users spend iterating through multiple queries while maintaining high result accuracy.
3Adaptability or versatility
If conventional search systems are used, then system simplicity is maintained, but the ability to understand natural language critiques is lost
Solution Approach 1:
The patent segments the complex natural language understanding task into manageable components handled by specialized neural networks. Each network focuses on specific aspects such as sentiment analysis, entity recognition, or query rewriting, making the overall complex system more maintainable and easier to debug while achieving high adaptability.
Data Source
AI summary
Systems and methods for image searching are described. The systems and methods include receiving a search query comprising user input for a reference image; converting the user input for the reference image to a preference statement using a machine learning model; encoding the preference statement in an embedding space to obtain an encoded preference statement; combining the encoded preference statement with an encoded reference image representing the reference image in the embedding space to obtain a multi-modal search encoding; and performing a search operation using the multi-modal search encoding to retrieve a second image, wherein the second image differs from the reference image based on the user input for the reference image.