Image Search Vector Attribute Control
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Solution Overview
Problem
Current image-based search systems lack the ability to fine-tune search attributes and control the emphasis on specific features, leading to unsatisfactory search results, as users cannot specify or adjust the level of importance for attributes like facial expressions or other image characteristics during the search process.
Innovation Solution
The system generates a full query vector from a query image and projects it into a reduced dimensional space, allowing users to specify preference and intensity values for attributes, which are then used to identify target images by modifying the query vector based on user-defined characteristics and emphasizing certain attributes during the search process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional image-based search is used, then search speed and simplicity are maintained, but users cannot fine-tune search attributes or control emphasis on specific features
Solution Approach 1:
The patent segments the image search problem into distinct attribute dimensions (e.g., facial expression, pose, lighting) that can be independently controlled. Each attribute is represented as a separate dimension in the vector space, allowing users to adjust preferences for individual attributes without affecting others. This segmentation enables fine-grained control over search results while maintaining system manageability through modular attribute handling.
Solution Approach 2:
The patent transforms image search from traditional keyword-based or holistic similarity matching into a multi-dimensional attribute space. By representing images as vectors with multiple attribute dimensions, the system enables users to navigate and control search results along specific dimensional axes. This dimensional transformation allows independent adjustment of attribute emphasis (e.g., prioritizing facial expression over pose) and provides geometric interpretation of attribute relationships through vector operations.
2Measurement precision
If attribute fine-tuning capability is added to image search, then search precision and user control are improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing attribute vectors for images in the database before actual search queries. Attribute directions and preference vectors are established in advance, allowing the search system to quickly compute results by comparing query vectors against pre-processed database vectors. This preprocessing eliminates the need for complex real-time attribute analysis during search execution, significantly reducing computational power requirements while maintaining high precision.
Solution Approach 2:
The patent enables dynamic parameter changes by allowing users to adjust attribute preference values and intensity weights without reprocessing the entire image database. The system computes search results by combining pre-stored attribute vectors with user-specified preference parameters through efficient vector operations. This parameter-based control approach achieves high search precision through fine-grained attribute tuning while avoiding computationally expensive re-analysis of image content.
3Ease of operation
If multiple attribute dimensions are introduced for control, then user control and search relevance are enhanced, but system complexity and processing overhead increase
Solution Approach 1:
The patent introduces attribute vectors and preference vectors as intermediary elements between user intent and image retrieval. Instead of directly implementing complex multi-attribute filtering logic, the system uses vector mathematics as an intermediary mechanism to handle attribute comparisons and combinations. The attribute direction vectors serve as mediators that encode semantic relationships between different image attributes, allowing the system to process multiple dimensions through unified vector operations rather than separate complex rule systems.
Data Source
AI summary
A query image is received, along with a query to initiate a search process to find other images based on the query image. The query includes a preference value associated with an attribute, the preference value indicative of a level of emphasis to be placed on the attribute during the search. A full query vector, which is within a first dimensional space and representative of the query image, is generated. The full query vector is projected to a reduced dimensional space having a dimensionality lower than the first dimensional space, to generate a query vector. An attribute direction corresponding to the attribute is identified. A plurality of candidate vectors of the reduced dimensional space is searched, based on the attribute direction, the query vector, and the preference value, to identify a target vector of the plurality of candidate vectors. A target image, representative of the target vector, is displayed.