Cross-Domain Image Retrieval with Adversarial Feature Alignment
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Solution Overview
Problem
Conventional image retrieval systems using deep learning struggle to accurately identify similarities between images with differing domain characteristics, such as real photographs and drawings, leading to unreliable results in assessing intellectual property rights.
Innovation Solution
An image retrieval method utilizing a trained artificial neural network with generative and discriminative models, employing adversarial learning to minimize domain differences and enhance feature vector clustering, allowing for accurate similarity assessment between real photographs and drawings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional image retrieval techniques using CNNs are applied to retrieve images with differing domain characteristics (e.g., real photographs and drawings), then the system can process images efficiently, but the similarity score between feature vectors becomes very low due to domain gap
Solution Approach 1:
The patent introduces a domain adaptation module as an intermediary component that bridges the domain gap between real photographs and drawings. This module includes a domain classifier that identifies image domains and a domain adapter that transforms feature vectors from different domains into a unified representation space, enabling accurate similarity assessment across domains while maintaining retrieval efficiency
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the feature extraction and similarity calculation parameters based on domain characteristics. The system modifies the embedding dimensions, normalization methods, and similarity metrics according to the detected domains, allowing efficient processing while maintaining high accuracy in cross-domain similarity assessment
2Device complexity
If feature vectors are extracted using the same neural network for both real images and drawings, then the processing pipeline remains simple, but the domain gap causes significant similarity score degradation
Solution Approach 1:
The patent segments the image retrieval system into distinct functional modules: a domain classifier to identify image domains, a domain adapter to adjust feature representations, and a similarity calculator to compute accurate similarity scores. This segmentation allows each module to specialize in specific tasks, improving measurement precision while keeping the overall pipeline manageable through modular architecture
3Reliability
If the system is designed to handle only images within the same domain (e.g., only real photographs or only drawings), then the similarity assessment remains accurate, but the system loses versatility in handling diverse image formats
Solution Approach 1:
The patent implements universality by designing a unified image retrieval system that can handle multiple domains (real photographs, drawings, and other image formats) through a single integrated architecture. The domain adapter and transformed feature vector database enable the system to perform accurate similarity assessment across different domains, providing versatile cross-domain image retrieval capability while maintaining high reliability
Data Source
AI summary
The present invention relates to an image retrieving method for identifying images similar to an image of a particular object among images with differing domain characteristics, such as drawing images for an object or photographs images taken of the object.