Machine Learning Cropping for Automated Image and Video Frame Selection
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Solution Overview
Problem
The manual process of image cropping and video frame selection is tedious, burdensome, and inefficient, particularly for large volumes of images, and lacks the ability to automate the application of artistic and aesthetic judgement.
Innovation Solution
A machine learning predictor program is trained using training raw images associated with sets of training master images to predict cropping characteristics, which are then applied to runtime images to generate cropped versions and select optimal video frames, with confidence levels indicating the quality of the predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual image cropping and video frame selection is performed, then artistic and aesthetic judgement can be applied, but the process is tedious, burdensome, and inefficient for large volumes of images
Solution Approach 1:
The system enables self-service by training the machine learning model to automatically perform cropping decisions without human intervention. The model learns from training data and independently determines optimal crop regions for new images, eliminating the need for manual cropping operations.
Solution Approach 2:
The patent replaces the mechanical manual process of image cropping with an automated machine learning system. The ML model substitutes human operators by automatically analyzing images and determining crop boundaries based on learned patterns from training data.
2Productivity
If automated cropping is implemented without machine learning, then efficiency improves, but the ability to apply artistic and aesthetic judgement is lost
Solution Approach 1:
The system performs preliminary action by training the machine learning model in advance with labeled training data that includes correct crop annotations. This pre-training enables the model to learn artistic and aesthetic cropping principles before being deployed for automated cropping operations.
Solution Approach 2:
The training process incorporates feedback mechanisms where the model learns from labeled training data showing correct crop regions. The model receives feedback during training to adjust its parameters and improve its cropping predictions, enabling it to replicate artistic judgment.
3Measurement precision
If machine learning training is performed with large datasets, then prediction accuracy improves, but training time and computational resources increase
Solution Approach 1:
The system applies partial action by using a representative subset of training data that captures the essential cropping patterns without requiring exhaustive datasets. The model learns sufficient cropping principles from curated training examples to achieve accurate predictions without the need for massive training corpora.
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.