Automatic Image Cropping Using ROI Ensemble Evaluation
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Solution Overview
Problem
Existing image cropping techniques lack flexibility and are unable to integrate sophisticated rules for determining important salient areas, and region-based cropping techniques are limited to specific training, such as face detection, failing to generate crop candidates for images without relevant content.
Innovation Solution
A crop generation system that evaluates multiple types of salient visual content by generating and evaluating crop candidates based on region of interest (ROI) ensembles, using saliency data to identify important areas and preserve visual content and composition, allowing for rapid evaluation of a large number of candidates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional cropping techniques are used, then the cropping process is simple, but the system lacks flexibility and cannot integrate sophisticated rules for determining important salient areas
Solution Approach 1:
The patent segments the image into multiple regions of interest (ROIs) based on different saliency detection methods. Each ROI represents a different important area identified by different algorithms, allowing the system to evaluate crop candidates against multiple segmented regions rather than treating the image as a whole. This segmentation enables flexible rule integration while managing complexity through modular processing of individual ROIs.
Solution Approach 2:
The patent creates a universal evaluation framework that can handle multiple types of saliency data and cropping rules through a single ensemble evaluation system. The framework universally applies to different image types and cropping requirements by integrating multiple saliency detection methods and evaluating crop candidates against all ROIs simultaneously, providing adaptability without proportionally increasing system complexity.
2Adaptability or versatility
If region-based cropping techniques are used, then face detection can be performed, but the system fails to generate crop candidates for images without specific training content
Solution Approach 1:
The patent merges multiple saliency detection methods into an ensemble that works together to identify regions of interest. By combining different detection approaches (e.g., face detection, object detection, color-based saliency, texture-based saliency), the system ensures reliable crop candidate generation for diverse image types. The ensemble approach means that if one method fails for a particular image type, other methods can compensate, maintaining reliability across all image categories.
Solution Approach 2:
The evaluation framework is designed to be universal, accepting multiple types of saliency data and applying the same evaluation logic regardless of the image content. The system can process images with faces, landscapes, objects, or abstract content using the same multi-method ensemble approach, ensuring reliable crop candidate generation for any image type without requiring separate specialized systems.
3Measurement precision
If multiple crop candidates are evaluated manually, then accurate selection can be achieved, but the process requires additional time and user effort
Solution Approach 1:
The system performs self-service by automatically evaluating crop candidates against multiple regions of interest and selecting the best candidates without requiring manual user review. The ensemble evaluation framework autonomously computes scores for each crop candidate based on how well they preserve visual content across all ROIs, and the system automatically generates the final cropped image or presents top candidates, eliminating the need for time-consuming manual evaluation while maintaining high selection accuracy.
Solution Approach 2:
The system implements feedback by using the evaluation scores from the ensemble of ROIs to automatically refine and select the best crop candidates. The feedback loop compares each crop candidate against all saliency-based ROIs, computes preservation scores, and uses this information to rank and select optimal candidates, achieving accurate automated selection that eliminates manual review time while maintaining precision through iterative score-based evaluation.
Data Source
AI summary
A crop generation system determines multiple types of saliency data and multiple crop candidates for an image. Multiple region of interest (“ROI”) ensembles are generated, indicating locations of the salient content of the image. For each crop candidate, the crop generation system calculates an evaluation score. A set of crop candidates is selected based on the evaluation scores.