Deep Neural Network Image Matching with Spatial Selectivity
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Solution Overview
Problem
Conventional image analysis systems are inaccurate and inflexible in identifying similar digital images, as they focus solely on semantic content and require specific input for matching, leading to inefficient user interactions and inability to tailor searches beyond individual object matching.
Innovation Solution
A deep neural network-based model is used to identify digital images based on visual attributes such as spatial selectivity, image composition, and object count, allowing for flexible matching of multiple query images and emphasizing user-selected features through image masks, generating compound feature vectors for accurate and efficient similarity searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image analysis systems rely solely on semantic content to classify images, then the systems can identify the same objects in two different digital images, but the systems produce inaccurate results when determining the visual similarity of two images
Solution Approach 1:
The system segments the image analysis process into multiple independent attribute analyses: semantic content analysis, spatial arrangement analysis, background analysis, and visual attribute analysis. Each aspect is analyzed separately and then integrated to produce comprehensive similarity results, resolving the contradiction by enabling both accurate object identification and flexible multi-attribute matching.
Solution Approach 2:
The system creates a composite feature representation that combines multiple types of image attributes (semantic content, spatial relationships, background characteristics, and visual properties) into a unified similarity assessment framework. This composite approach enables the system to achieve both high accuracy in identifying similar images and flexibility in accommodating different matching criteria.
2Ease of operation
If conventional image analysis systems require very specific input (e.g., a single digital image) to use as basis for finding matching images, then the systems can perform single-image searches, but the systems require performing multiple single-image searches and/or retraining an analysis model to accommodate multiple input images
Solution Approach 1:
The system implements a universal image matching framework that can handle multiple query images simultaneously without requiring separate searches or model retraining. The same analysis model processes single images, multiple images, or composite queries uniformly by integrating features from all input images, thereby reducing user effort and time while maintaining ease of operation.
Solution Approach 2:
The system merges multiple query images into a unified feature representation by combining their respective attribute vectors. This allows users to perform multi-image searches in a single operation rather than conducting multiple separate searches, significantly reducing the time and effort required while maintaining simple interaction.
3Measurement precision
If conventional image analysis systems are one-dimensional in that they only match digital images based on identifying particular objects within the images, then the systems can perform object-focused analysis, but the systems disregard other aspects of the images (e.g., backgrounds, spatial arrangement of objects, and other visual attributes)
Solution Approach 1:
The system segments the complex image analysis task into distinct modular components: object detection module, spatial relationship analysis module, background analysis module, and visual attribute analysis module. Each module handles a specific aspect independently, improving accuracy through comprehensive analysis while managing complexity through modular design and clear separation of concerns.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing a deep neural network-based model to identify similar digital images for query digital images. For example, the disclosed systems utilize a deep neural network-based model to analyze query digital images to generate deep neural network-based representations of the query digital images. In addition, the disclosed systems can generate results of visually-similar digital images for the query digital images based on comparing the deep neural network-based representations with representations of candidate digital images. Furthermore, the disclosed systems can identify visually similar digital images based on user-defined attributes and image masks to emphasize specific attributes or portions of query digital images.