Distractor Mitigation in Images Using Multi-Cue Classification
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Solution Overview
Problem
Existing image processing techniques fail to effectively remove unwanted people from images, often mistakenly removing intended subjects or leaving unintended distractors, and manual editing is time-consuming and laborious, especially for large collections of photos.
Innovation Solution
A distractor mitigation system that identifies and classifies individuals in images based on salience cues, recognition cues, and distractor cues, allowing for automatic removal or modification of unwanted people by blurring or replacing them with background pixels, while preserving intended subjects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a machine learning model is used to automatically remove distractors from images, then productivity is improved, but measurement precision deteriorates because the model cannot reliably distinguish between unwanted people and intended subjects
Solution Approach 1:
The system segments the image analysis task into multiple independent components: salient region detection, person detection, cue generation (salience cues, recognition cues, distractor cues), and classification. Each component operates independently and contributes to the final decision, allowing the system to process images automatically while maintaining high precision through multi-factor evaluation
Solution Approach 2:
The system changes the parameters used for distractor identification by introducing multiple types of cues (salience cues based on region importance, recognition cues based on person identification, distractor cues based on spatial relationships) instead of relying on a single parameter. This multi-parameter approach enables automatic processing while improving classification accuracy
2Measurement precision
If manual editing is used to remove unwanted people from images, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
The system performs self-service by automatically executing the distractor removal process without human intervention. It independently detects salient regions, identifies people, generates multiple types of cues, classifies unwanted individuals, and removes them from images. This automation maintains high precision while eliminating the time-consuming manual editing process
Solution Approach 2:
The system replaces the mechanical manual editing process with an automated computational system. Instead of manual inspection and editing, the system uses algorithmic processing to detect, classify, and remove unwanted people, substituting human labor with automated image processing techniques that maintain accuracy while dramatically reducing time loss
3Productivity
If existing automated techniques remove people from images, then productivity is improved, but reliability deteriorates because intended subjects are mistakenly removed
Solution Approach 1:
The system applies local quality by generating salient region maps that identify specific areas of the image that are important to the overall composition. By analyzing the spatial relationship between detected people and these salient regions, the system can locally evaluate whether each person is likely an intended subject or an unwanted distractor, improving reliability while maintaining automated processing
Solution Approach 2:
The system uses feedback mechanisms by generating multiple types of cues (salience cues indicating region importance, recognition cues indicating person identity, distractor cues indicating spatial relationships) and combining them to make classification decisions. This multi-cue feedback approach allows the system to reliably distinguish between intended subjects and unwanted people, preventing mistaken removal while maintaining high productivity
Data Source
AI summary
Systems, methods, and software are described herein for removing people distractors from images. A distractor mitigation solution implemented in one or more computing devices detects people in an image and identifies salient regions in the image. The solution then determines a saliency cue for each person and classifies each person as wanted or as an unwanted distractor based at least on the saliency cue. An unwanted person is then removed from the image or otherwise reduced from the perspective of being an unwanted distraction.