Repeated Distractor Detection in Digital Images
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Solution Overview
Problem
Conventional techniques for addressing distractors in digital images face challenges in identifying distractors across various objects and scenarios, and they struggle with manually selecting a large number of distractors, leading to errors and inefficient resource use.
Innovation Solution
A distractor detection system that uses a machine-learning model to identify input distractors based on user input coordinates, detects candidate distractors using patch-matching techniques, and verifies their similarity to the input distractors through image feature comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional techniques are used to address distractors, then the system can identify distractors in simple scenarios, but it fails to accurately identify distractors across diverse objects and scenarios
Solution Approach 1:
The system changes the parameter of feature extraction by using a machine learning model that adapts to different object types and scenarios. Instead of fixed detection rules, the model learns to identify distractor characteristics across diverse contexts, improving both adaptability and reliability simultaneously.
Solution Approach 2:
The system creates a virtual copy of the distractor detection process by using patch-matching techniques. It extracts features from the input distractor and searches for matching patches throughout the image, effectively copying the detection logic to find all instances of similar distractors.
2Productivity
If manual selection of distractors is performed, then users can control the selection process, but it becomes inefficient and error-prone when dealing with a large number of distractors
Solution Approach 1:
The system performs self-service by automatically detecting and selecting distractors without requiring manual intervention. The machine learning model autonomously identifies distractors and the patch-matching technique automatically finds all instances, eliminating the need for manual selection and significantly improving productivity while reducing time loss.
Solution Approach 2:
The system performs preliminary action by pre-processing the image to extract features and identify potential distractors before final selection. The machine learning model pre-identifies candidate distractors and the patch-matching technique pre-finds matching patterns, allowing for efficient automated selection without manual review of each distractor.
3Speed
If automated distractor detection is implemented, then processing speed increases, but verification accuracy decreases due to false positives
Solution Approach 1:
The system implements feedback by using the patch-matching technique to verify candidate distractors against the original input distractor features. The extracted features from candidate patches are compared with the input distractor features, providing feedback to confirm or reject candidates, thus maintaining high verification accuracy while preserving fast automated detection speed.
Solution Approach 2:
The system replaces mechanical verification methods with computational feature extraction and comparison. Instead of manual verification, the system uses machine learning-based feature extraction and mathematical comparison of feature vectors to verify accuracy, achieving both speed and precision through computational rather than mechanical processes.
Data Source
AI summary
Repeated distractor detection techniques for digital images are described. In an implementation, an input is received by a distractor detection system specifying a location within a digital image, e.g., a single input specifying a single set of coordinates with respect to a digital image. An input distractor is identified by the distractor detection system based on the location, e.g., using a machine-learning model. At least one candidate distractor is detected by the distractor detection system based on the input distractor, e.g., using a patch-matching technique. The distractor detection system is then configurable to verify that the at least one candidate distractor corresponds to the input distractor. The verification is performed by comparing candidate distractor image features extracted from the at least one candidate distractor with input distractor image features extracted from the input distractor.