Dynamic Relation Tree Construction from Image Collections
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Solution Overview
Problem
Existing technologies lack an efficient method to analyze and visualize the dynamic relationships among individuals in large collections of images over time, which is crucial for applications like image management, social networking, and homeland security.
Innovation Solution
A system and method that uses face clustering, facial feature analysis, and contextual information to construct a dynamic relation tree, which represents the positions of main characters in relation circles and changes with different time periods, providing accurate and evolving relationship information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional image analysis methods are used on large image collections, then individual images can be processed, but the dynamic relationships among individuals over time cannot be effectively analyzed
Solution Approach 1:
The patent segments the large image collection into multiple subsets based on temporal information (time periods). Each subset is processed independently to extract relationship information, which is then integrated to form a comprehensive dynamic relationship view. This segmentation enables efficient processing of large collections while preserving relationship information across different time periods.
Solution Approach 2:
The patent introduces a temporal dimension to traditional image analysis by organizing images according to time information and constructing dynamic relationship trees that evolve over time. This transforms static relationship analysis into dynamic multi-dimensional analysis, enabling the system to capture how relationships change across different time periods while maintaining analysis efficiency.
2Measurement precision
If comprehensive relationship analysis is performed on all images, then accurate relationship information can be obtained, but the system complexity and processing time increase significantly
Solution Approach 1:
The patent divides the comprehensive relationship analysis task into smaller sub-tasks by processing images in temporal subsets. Each subset undergoes focused relationship extraction, and results are integrated to achieve accurate overall relationship estimation. This segmentation reduces system complexity by breaking down the monolithic analysis process into manageable stages.
Solution Approach 2:
The patent performs preliminary organization of images based on temporal information before conducting relationship analysis. By pre-grouping images into time-based subsets and identifying key individuals in advance, the system reduces the complexity of the main analysis task while maintaining measurement precision through structured processing.
3Adaptability or versatility
If static relationship analysis is used, then simple processing can be achieved, but the dynamic changes in relationships over time are lost
Solution Approach 1:
The patent transforms static relationship analysis into a dynamic process by constructing relationship trees that evolve across different time periods. The system adapts to temporal changes by re-processing image subsets as new images become available or as time periods are adjusted, maintaining versatility while using efficient subset-based processing to preserve productivity.
Solution Approach 2:
The patent implements periodic relationship analysis by dividing the image collection into time periods and processing each period systematically. This periodic approach enables the system to capture dynamic relationship changes over time while maintaining processing efficiency through structured, repeatable analysis cycles on manageable subsets.
Data Source
AI summary
A system and method are provided for determining a dynamic relation tree based on images in an image collection. An example system includes a memory for storing computer executable instructions, and a processing unit for accessing the memory and executing the computer executable instructions. The computer executable instructions include an event classifier to classify main characters of images in an image collection as to an event identification based on events in which the main characters appear, wherein each main character is characterized as to at least one attribute; a relation determination engine to determine relation circles of the main characters; and a construction engine to construct a dynamic relation tree representative of relations among the main characters, where the dynamic relation tree provides representations of the positions of the main characters in the relation circles, and where views of the dynamic relation tree change when different time periods are specified.