Active Context-Based Concept Fusion for Image Detection
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Solution Overview
Problem
Existing methods for semantic concept detection in images and videos face challenges in enhancing accuracy due to insufficient data for learning concept relationships, unreliable detectors, and the complexity of concept relations, leading to inconsistent performance gains from context-based concept fusion strategies.
Innovation Solution
The active context-based concept fusion method automatically determines contextual relationships between concepts and utilizes user-provided ground truth labels to refine detection results, selectively annotating key concepts based on mutual information and detector performance to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If context-based concept fusion is applied to improve concept detection accuracy, then detection accuracy improves for some concepts, but the system complexity increases and requires large amounts of training data
Solution Approach 1:
The patent segments the complex concept detection task by identifying and separating key contextual concepts from the overall scene. Instead of attempting to detect all concepts simultaneously with equal complexity, the system focuses on detecting specific key concepts (such as outdoor/indoor scene, face presence) that have high mutual information with the target concept. This segmentation reduces the effective complexity of the detection system while maintaining accuracy improvements through contextual fusion.
Solution Approach 2:
The patent applies local quality by making different parts of the detection system have different levels of complexity and data requirements. Key contextual concepts that provide the most benefit are detected with higher priority and used to refine detection of the target concept. Not all concepts are treated equally - only those with significant mutual information are selected for active user labeling and contextual fusion, creating a localized optimization strategy that improves accuracy without uniformly increasing system complexity.
2Measurement precision
If context-based concept fusion uses large amounts of training data to learn concept relationships, then detection accuracy improves, but the requirement for training data increases
Solution Approach 1:
The patent extracts and focuses only on the most important contextual relationships rather than attempting to learn all possible concept correlations. By calculating mutual information between the target concept and other concepts, the system identifies and extracts only those contextual relationships that provide significant benefit. This extraction approach allows the system to achieve accuracy improvements using a limited subset of training data focused on key concept pairs, rather than requiring comprehensive training data for all possible concept relationships.
Solution Approach 2:
The patent implements partial action by selectively applying context-based fusion only to concepts where it provides significant benefit, rather than uniformly applying it to all concepts. The system calculates mutual information metrics to determine which contextual concepts are worth the computational expense of fusion. This partial application strategy achieves accuracy improvements for key concepts without requiring the system to process and learn from all possible concept relationships, reducing the overall training data requirement.
3Measurement precision
If active user labeling is implemented to provide ground truth for key concepts, then detection accuracy improves, but user interaction requirements increase
Solution Approach 1:
The patent applies partial action by requesting user labeling only for specific key concepts rather than all concepts in the image. The system uses mutual information calculations to identify which contextual concepts will provide the most benefit for detecting the target concept, and only prompts the user to label those specific concepts. This selective approach maintains accuracy improvements while minimizing user interaction requirements, as users are asked to provide input only where it matters most for the detection task.
Solution Approach 2:
The patent introduces an intermediary computational layer that automatically calculates mutual information and selects which concepts require user labeling. This intermediary system acts as a mediator between the raw image data and the user, intelligently determining which contextual concepts need human verification. The intermediary reduces the user's burden by filtering out concepts that can be reliably detected automatically, presenting only those concepts where user input would provide the most value.
Data Source
AI summary
A context-based concept fusion method detects a first concept in an image record. The method includes automatically determining at least one other concept in the image record which has a contextual relationship with the first concept and which is to be labeled by a user of the method; and labeling the at least one other concept by the user with a ground truth label to be used in the context-based concept fusion method to improve detection of the first concept in the image record.