ML Predictor for Automated Image Cropping
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Solution Overview
Problem
Manual image cropping is tedious and inefficient for content providers who need to create multiple versions of images with different cropping characteristics, and there is a lack of automated solutions that can replicate human artistic and aesthetic judgment.
Innovation Solution
A machine learning predictor program is trained on sets of training raw images with associated cropping characteristics to predict cropping boundaries for input images, enabling automated generation of multiple cropped versions and selection of optimal video frames based on statistical confidence levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual image cropping is performed to create multiple versions with different cropping characteristics, then artistic and aesthetic quality can be maintained, but the process becomes tedious and inefficient
Solution Approach 1:
The system enables self-service automated cropping by training a machine learning model on example cropped images. The trained model then autonomously predicts cropping characteristics for new images without requiring manual human intervention, allowing the system to serve itself in generating multiple cropped versions efficiently while maintaining quality standards learned from training data
Solution Approach 2:
The system creates copies of cropping characteristics from training master images and applies them to new raw images. By copying the cropping patterns, boundaries, and aesthetic decisions from trained examples, the system can generate multiple cropped versions rapidly while preserving the artistic quality embedded in the training data
2Productivity
If automated cropping solutions are implemented to improve efficiency, then productivity increases, but the ability to replicate human artistic and aesthetic judgment is lacking
Solution Approach 1:
The system incorporates feedback by training the machine learning model on sets of training raw images with associated training master images that indicate expected cropping characteristics. The model learns from this feedback loop, continuously improving its ability to predict cropping boundaries and characteristics that match human aesthetic judgment, thereby maintaining quality while achieving automation
Solution Approach 2:
The trained machine learning model acts as an intermediary between human-created training data and automated cropping operations. It translates human artistic decisions encoded in training master images into automated prediction capabilities, bridging the gap between manual quality and automated efficiency by mediating the cropping process through learned patterns
3Adaptability or versatility
If multiple cropped versions are generated for each runtime raw image, then versatility and adaptability improve, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning model on extensive training data before deployment. This upfront preparation enables the model to rapidly generate multiple cropped versions during runtime without requiring extensive computational resources for each individual cropping operation, as the heavy learning work was已完成 during the training phase
Data Source
AI summary
Example systems and methods may selection of video frames using a machine learning (ML) predictor program are disclosed. The ML predictor program may generate predicted cropping boundaries for any given input image. Training raw images associated with respective sets of training master images indicative of cropping characteristics for the training raw image may be input to the ML predictor, and the ML predictor program trained to predict cropping boundaries for raw image based on expected cropping boundaries associated training master images. At runtime, the trained ML predictor program may be applied to runtime raw images in order to generate respective sets of runtime cropping boundaries corresponding to different cropped versions of the runtime raw image. The runtime raw images may be stored with information indicative of the respective sets of runtime boundaries.