Graph-Based Color Description Generation Using Message Passing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods lack an effective way to generate natural language descriptions for colors, especially in the context of human perception, making it difficult to accurately name colors, describe color compositions, and communicate color information in images.
Innovation Solution
A graph-based method is employed to structure color relationships, where nodes represent colors and edges are added based on human perception thresholds, with external databases filtering edges and message passing algorithms learning feature vectors for new nodes, which are then used to generate descriptions using a recurrent neural network-based decoder in a semi-supervised fashion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a graph-based method with message passing is used to learn color features, then color naming accuracy and description quality are improved, but computational complexity and processing time increase
Solution Approach 1:
The method segments the color description task into distinct computational stages: graph construction from color tuples, edge filtering using external databases, message passing for feature learning, and RNN-based description generation. This segmentation allows each component to be optimized independently while maintaining overall accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-processing color data into structured tuples and pre-filtering graph edges using external color comparative databases before the main message passing and description generation stages. This reduces the complexity of subsequent processing while preserving color relationships.
2Reliability
If external color comparative databases are used to filter graph edges, then color relationship accuracy is improved, but data processing time and storage requirements increase
Solution Approach 1:
External color comparative databases are used in advance to filter and validate graph edges before the main processing pipeline. This preliminary filtering ensures that only color relationships supported by external evidence are included, improving reliability while reducing the workload for subsequent message passing operations.
3Measurement precision
If a semi-supervised learning approach with RNN decoder is used for description generation, then natural language description quality is improved, but training complexity and computational resources increase
Solution Approach 1:
The patent introduces an intermediary message passing mechanism that bridges the graph-structured color data and the RNN decoder. This intermediary layer learns color features through iterative message passing and provides refined feature vectors to the RNN, enabling high-quality description generation while simplifying the training process compared to direct end-to-end learning.
4Measurement precision
If color tuples are structured as graph nodes with perception-based edges, then color perception accuracy is improved, but graph construction complexity increases
Solution Approach 1:
The patent transforms color data from simple tuples into a graph structure where nodes represent colors and edges represent perceptual relationships. By changing the data representation parameters from flat tuples to graph topology with perception-based edge weights, the system captures color relationships more accurately while managing construction complexity through systematic processing.
Data Source
AI summary
A method, system and computer program product are presented for generating a description in natural language for a color. The method includes acquiring a list of tuples, generating a graph by using each of the tuples as a node and adding edges between the nodes when a difference between colors of the nodes in terms of human perception is outside a predetermined range, filtering the edges based on external color comparative descriptions, incorporating a new node in the graph by finding a closest neighbor node based on the color difference and adding a new edge between the new node and the closest neighbor node, learning a feature vector for each of the nodes by using message passing and the colors of the nodes as initial seeds, and generating a description of the new node by using each of the learned feature vectors as initial states for a neural-network based decoder.