Image Region Localization via Graph Convolutional Network
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Solution Overview
Problem
Current methods for locating image regions corresponding to natural sentences in images fail to accurately consider spatial relationships between local regions, leading to reduced image understanding capabilities due to the neglect of semantic information between local regions.
Innovation Solution
The method employs a Graph Convolutional Network (GCN) to enhance semantic representations of image candidate regions and determine a target image region by building associations between semantic information, using an image region locating network model to measure matching degrees between text features and enhanced semantic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object proposal methods are used to extract candidate regions, then the process is simple and fast, but the accuracy of image region localization is reduced due to neglecting spatial relationships between regions
Solution Approach 1:
The patent segments the image into multiple candidate regions using object proposal methods, then processes each region separately through semantic feature extraction and spatial relationship modeling. This segmentation allows the system to focus computational resources on relevant regions while maintaining overall accuracy.
Solution Approach 2:
The patent introduces a spatial relationship dimension by modeling positional relationships between candidate regions and text elements. This additional dimensional information (spatial coordinates and relative positions) enhances the localization accuracy beyond what traditional flat feature matching provides.
2Productivity
If semantic information between local regions is neglected to simplify processing, then the computation is faster and easier, but image understanding capabilities are reduced
Solution Approach 1:
The patent merges semantic features from multiple candidate regions with their spatial relationships into a unified representation. This combination allows the system to process regions efficiently while simultaneously capturing their interrelationships, maintaining both speed and understanding capability.
Solution Approach 2:
The patent introduces spatial relationship features as an intermediary that bridges semantic information from different regions. This mediator enables the system to efficiently compute relationships between regions without directly processing all pairwise interactions, thus maintaining processing speed while improving image understanding.
3Measurement precision
If manual extraction of regions associated with natural sentences is performed, then errors are minimized, but a lot of time is consumed and it cannot scale to large numbers of images
Solution Approach 1:
The patent implements a self-service system where the machine automatically performs region localization by learning spatial relationships between text and image regions. The system serves itself by using trained models to identify regions without human intervention, achieving both high accuracy and scalability to large image datasets.
Solution Approach 2:
The patent performs preliminary training of the localization model on labeled data to learn spatial relationships between text and image regions. This preliminary action enables the system to quickly and accurately locate regions in new images without time-consuming manual extraction, achieving both precision and efficiency.
Data Source
AI summary
The present disclosure provides a method for locating an image region. The method comprises determining one or more regions in an image. Each of the regions corresponding to a respective candidate object in the image. The method comprises generating respective semantic information having a one-to-one correspondence with a corresponding candidate object in each of the regions. The method comprises obtaining respective enhanced semantic information corresponding to the respective semantic information using a graph convolutional network (GCN). A matching degree between a text feature set corresponding to a to-be-located text and each of the respective enhanced semantic information is obtained using an image region locating network model. The method further comprises determining a target image candidate region from the one or more regions according to the matching degree between the text feature set and each of the respective enhanced semantic information.