Image Classifier Segmentation for Automated Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image analysis techniques are overly human-intensive and resource-intensive, failing to effectively leverage previous analyses across multiple experts and systems, leading to inefficiencies in processing and statistical information gathering.
Innovation Solution
An information processing system with an image classifier comprising image planner, scrutinizer, and aggregator elements that organizes and processes image data in a distributed manner, enabling efficient and accurate analysis by forming logical groups for classification, and facilitating massive parallel processing across geographically distributed servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional image analysis techniques are used, then human expertise can be applied to detect and diagnose conditions, but the process becomes overly human-intensive and inefficient
Solution Approach 1:
The system segments the image analysis task into multiple independent components: image planner elements that organize data, image scrutinizer elements that perform classification, and image aggregator elements that consolidate results. This segmentation enables parallel processing across multiple experts while maintaining coordinated functionality, directly resolving the contradiction between automation and productivity.
Solution Approach 2:
The patent implements a hierarchical nested structure where image scrutinizer elements are organized within logical groups managed by image planner elements, which in turn are coordinated by image aggregator elements. This nested architecture allows scalable automation where smaller processing units can be independently managed while contributing to the overall automated system, improving both automation extent and processing efficiency.
2Productivity
If automated image analysis techniques are used, then processing speed increases, but excessive processor and memory resources are required
Solution Approach 1:
The system applies local quality by organizing image scrutinizer elements into logical groups with specific classification criteria, allowing each group to process only relevant image portions or categories. This targeted approach maintains high processing speed for specific tasks while reducing overall resource consumption compared to processing all images uniformly.
Solution Approach 2:
The patent implements dynamic resource allocation where image planner elements can organize scrutinizer elements into logical groups based on current processing needs and criteria. This dynamic reconfiguration allows the system to adjust processor and memory allocation in real-time, maintaining processing speed while optimizing resource usage based on actual task requirements.
3Loss of information
If results from multiple experts are aggregated, then statistical information can be gathered, but the results remain scattered across unrelated processing systems
Solution Approach 1:
The patent merges the results from multiple experts by implementing image aggregator elements that consolidate classification results from various image scrutinizer elements into a unified output. This merging mechanism gathers statistical information across all experts while maintaining a relatively simple system architecture through standardized aggregation protocols.
Solution Approach 2:
The system achieves universality through standardized interfaces and protocols that allow different expert systems to contribute their results to a common aggregation framework. The image aggregator elements can handle results from various scrutinizer elements with different classification criteria, enabling statistical analysis across diverse expertise while avoiding complex custom integration for each expert system.
Data Source
AI summary
An information processing system is configured for automated diagnostic analysis of digital images. The system comprises an image classifier implementing an image processing engine for performing at least a portion of a classification operation on at least a portion of an image. The image processing engine comprises an interconnection of at least one image planner element, a plurality of image scrutinizer elements and at least one image aggregator element. The image planner element is configured to organize the plurality of image scrutinizer elements into two or more logical groups of image scrutinizer elements based at least in part on one or more criteria to be used in performing the classification operation.