Image Search Metric Modification via Feature Interaction Analysis
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Solution Overview
Problem
Users face challenges in searching for images or products that are almost what they want, as existing search systems do not effectively allow for modifications to image features based on user interactions.
Innovation Solution
A system and method that allow users to interact with images by modifying features, such as using an image editing tool to change the collar of a shirt from a V-neck to a crew neck, and then using these interactions to determine features of interest. These features are used to modify search metrics, biasing subsequent image searches to find images with the desired features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users perform image searches with basic keywords, then search speed is maintained, but search accuracy and relevance to user preferences deteriorate
Solution Approach 1:
The system performs preliminary analysis of user interactions with image features before the actual search query is executed. By pre-processing interaction data to extract feature preferences and weighting factors, the system prepares optimized search parameters in advance, enabling faster and more accurate searches without requiring users to manually specify each preference.
Solution Approach 2:
The system implements a feedback mechanism where user interactions with image features (such as hovering, clicking, or editing specific parts of images) are continuously monitored and fed back into the search algorithm. This feedback loop dynamically adjusts search metrics and feature weights based on actual user behavior, progressively improving search accuracy while maintaining efficiency through learned patterns.
2Measurement precision
If the search system incorporates detailed user interactions and feature modifications, then search relevance improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of image search optimization into distinct modular components: interaction detection modules that capture user behaviors, feature extraction modules that identify relevant image attributes, metric modification modules that adjust search parameters, and search execution modules that perform the actual search. This segmentation allows each component to be developed and optimized independently, reducing overall system complexity while maintaining high search relevance.
Solution Approach 2:
The system introduces intermediary layers between user interactions and the core search algorithm. These intermediaries include interaction analysts that translate user actions into structured data, feature representers that convert image attributes into standardized formats, and metric adapters that bridge user preferences with search parameters. These intermediaries simplify the interface between complex user behaviors and the search engine, reducing system complexity while preserving search relevance.
3Measurement precision
If the system modifies search metrics based on image feature interactions, then search result quality improves, but computational resources increase
Solution Approach 1:
The system applies partial action by selectively modifying search metrics only for the specific image features that users actually interact with, rather than processing all possible features uniformly. When users show interest in certain features (through hovering, clicking, or editing), the system intensively analyzes and adjusts metrics for those particular features while using default or reduced processing for other features. This approach maintains high search result quality for relevant features while reducing overall computational resource consumption.
Data Source
AI summary
A system and method are provided for modifying search metrics based on features of interest determined from interactions with images. The method includes determining an input comprising at least one feature of interest, the at least one feature of interest determined from at least one interaction with a first image; and using the input in an image search by modifying a search metric to bias the image search towards locating one or more second images based on the at least one feature of interest.