Deep Neural Network Visual Attribute Manipulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing electronic content search methods, particularly in visual contexts, face challenges in refining queries to find visually similar items due to limitations in keyword-based approaches, which struggle with quantifying visual attributes and allowing attribute refinements, leading to inaccurate and incomplete search results.
Innovation Solution
The use of a single convolutional neural network with multi-label loss functions enables visual similarity searches and attribute manipulation, allowing users to modify visual attributes of an image query while preserving other attributes, thereby improving the accuracy of search results by generating superior visual similarity models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based approaches are used for visual content search, then the search system is simple to implement, but the accuracy of visual attribute identification and query refinement is poor
Solution Approach 1:
The patent replaces traditional keyword-based mechanical search systems with a deep neural network-based visual attribute recognition system. The CNN automatically extracts and identifies visual attributes from images, substituting manual keyword tagging and search with automated visual analysis, thereby improving measurement precision while accepting increased system complexity.
Solution Approach 2:
The system changes the parameter space from discrete keywords to continuous visual attribute vectors. By representing images as high-dimensional vectors capturing multiple visual attributes simultaneously, the system enables precise query refinement through parameter manipulation in the vector space, allowing users to modify specific attribute values while maintaining others.
2Adaptability or versatility
If image-based similarity searching is used, then visual similarity can be captured, but attribute refinements and query modifications are not allowed
Solution Approach 1:
The patent segments the global image similarity problem into individual attribute dimensions. Instead of treating the image as a single holistic unit, the system decomposes it into separate visual attributes (color, shape, texture, etc.), allowing independent manipulation of each attribute while maintaining overall visual similarity through the structured vector representation.
Solution Approach 2:
The system transitions from 2D image space to high-dimensional attribute vector space. This dimensional transformation enables users to navigate and refine queries by moving along specific attribute dimensions while maintaining constraints in other dimensions, providing versatile query refinement capabilities that were impossible in traditional image space.
3Measurement precision
If multiple neural networks are used for different visual attributes, then attribute recognition accuracy improves, but system complexity and computational overhead increase
Solution Approach 1:
The patent merges multiple attribute recognition functions into a single unified convolutional neural network. The CNN processes the entire image through shared feature extraction layers, then branches into multiple output layers for different visual attributes. This consolidation maintains high recognition accuracy for all attributes while reducing overall system complexity compared to using separate networks for each attribute.
Solution Approach 2:
The single neural network is designed with multi-functionality, serving as a universal attribute recognition system. The shared convolutional layers extract general visual features that are then used for multiple different attribute predictions, allowing one network to perform the work of many specialized networks while improving efficiency and reducing complexity.
Data Source
AI summary
Embodiments described herein are directed to allowing manipulation of visual attributes of a query image while preserving the visual attributes of a query image. A query image can be received and analyzed using a trained network to determine a set of items whose images demonstrate visual similarity to the query image across a plurality of visual attributes. Visual attributes of the query image may be manipulated to allow a user to search for items that incorporate the desired manipulated visual attributes while preserving the visual attributes of the query image. Content for at least a determined number of highest ranked, or most similar, items related to the modified visual attributes can then be provided.