Hierarchical Graph Model for Region Segmented Images
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image segmentation techniques fail to effectively model both spatial and semantic relationships among image regions, leading to complex and inefficient representations that are not generic enough for various applications, particularly when dealing with arbitrarily shaped regions.
Innovation Solution
A hierarchical graph representation model that includes nodes for individual and logical image regions, where each node represents an arbitrarily shaped region, and spatial relationships are captured through hierarchical and adjacent connections, forming a cohesive and compact structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If Region Adjacency Graph method is used to model image regions, then adjacency relationships among image regions are captured, but containment relationships are missed and complexity increases
Solution Approach 1:
The patent combines the Region Adjacency Graph and Region Adjacency Tree into a unified hierarchical graph model that captures both adjacency and containment relationships simultaneously. The hierarchical structure merges the strengths of both previous methods while eliminating their individual weaknesses, creating a compact representation that preserves all spatial relationships without increasing complexity.
Solution Approach 2:
The hierarchical graph model serves multiple functions: it represents both adjacency relationships (like the graph) and containment relationships (like the tree), while also providing a compact hierarchical structure for efficient processing. This multi-functional approach eliminates the need to choose between different modeling methods for different relationship types.
2Loss of information
If Region Adjacency Tree is used to model image regions, then containment relationships are captured, but adjacency relationships are missed resulting in loss of granularity
Solution Approach 1:
The patent merges the Region Adjacency Graph and Region Adjacency Tree into a unified hierarchical graph model that captures both adjacency and containment relationships simultaneously. The hierarchical structure merges the strengths of both previous methods while eliminating their individual weaknesses, creating a compact representation that preserves all spatial relationships without increasing complexity.
3Ease of manufacture
If Quad tree or HV tree models are used to partition images, then hierarchical structure is achieved, but arbitrarily shaped regions cannot be represented
Solution Approach 1:
The patent segments the image into arbitrarily shaped regions rather than forcing rectangular partitions. Each region is represented as a node in the hierarchical graph, allowing the model to adapt to the actual shapes and boundaries present in the image data while maintaining hierarchical organization for efficient processing.
Solution Approach 2:
The model transitions from static rectangular partitions to dynamic arbitrarily shaped regions that can adapt to the content of the image. The hierarchical graph structure allows regions to have flexible boundaries and shapes while maintaining the organizational benefits of hierarchical decomposition.
4Adaptability or versatility
If semantic relationship-based techniques are used to model images, then similarity of image region properties is established, but spatial relationships are ignored making them unsuitable for image understanding
Solution Approach 1:
The patent combines semantic relationship modeling with spatial relationship representation in a unified hierarchical graph model. The model simultaneously captures both the semantic properties of regions and their spatial relationships, enabling comprehensive image understanding that leverages both types of information together rather than separately.
Data Source
AI summary
A system and method for modeling a region segmented image is described. Aspects of the present invention may include the generation of a computer model that models the region segmented image, the computer model comprising one or more nodes, wherein each node in the one or more nodes represents an arbitrarily shaped region present in the region segmented image, and each of the arbitrarily shaped regions comprises an image segment wherein the image segment is an indivisible partition in the region segmented image. The model may additionally comprise one or more logical nodes, wherein each logical node represents an image region formed by the union of two or more arbitrarily shaped image regions in the region segmented image that exhibit at least one type of spatial relationship and a hierarchical graph representation of the region segmented image. Types of spatial relationships may include hierarchical, adjacent and cohesive spatial relationships.