Machine Learning Engine for Video Image Ranking
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Solution Overview
Problem
Current methods for selecting representative images for digital video items are inefficient, as manual selection is time-consuming, random selection yields uninteresting frames, and characteristic-based techniques fail to appeal to all users in all situations, making it difficult to drive video consumption effectively.
Innovation Solution
A machine learning engine is trained using a set of input parameters that include visual, temporal, contextual, and external features to rank candidate images based on model-generated scores, allowing for target-specific image selection that varies according to user demographics and behavior, ensuring the most representative images are chosen for video items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual selection of representative images is used, then the quality and appeal of selected images is improved, but the time consumption and processing cost increases
Solution Approach 1:
The system pre-processes video frames during encoding to extract candidate frames with specific characteristics (scene changes, key moments). This preliminary action prepares the data structure in advance, allowing the machine learning model to quickly rank and select the best representative image without manual intervention during actual video processing.
Solution Approach 2:
The patent replaces the manual mechanical selection process with an automated machine learning-based system. The machine learning engine automatically ranks candidate frames based on trained criteria, substituting human editors' manual selection with an automated computational process that maintains high quality while eliminating time consumption.
2Productivity
If random frame selection is used, then the processing speed is improved, but the representativeness and user interest of selected images deteriorates
Solution Approach 1:
The system changes the selection parameters by using machine learning-generated scores to rank candidate frames. Instead of random selection, the system evaluates frames based on multiple parameters including visual characteristics, temporal position, and scene change detection. This parameter-based ranking ensures that the selected image is both representative and interesting to users while maintaining automated processing speed.
3Device complexity
If frames are selected at predetermined offsets, then the processing complexity is reduced, but the quality and user engagement of selected images deteriorates
Solution Approach 1:
The patent segments the video into candidate frames based on specific criteria such as scene changes and temporal intervals. Instead of selecting a single frame at a predetermined offset, the system divides the video into multiple candidate segments and then ranks them using machine learning. This segmentation approach maintains low processing complexity while significantly improving image quality and user engagement.
4Adaptability or versatility
If external images identifying generic concepts are used, then the publisher's market awareness is improved, but the user's interest in the specific video content deteriorates
Solution Approach 1:
The system applies local quality by selecting representative images that are specific to each video's content rather than using generic external images. The machine learning engine analyzes the actual video frames and selects images that accurately represent the specific video content, ensuring relevance to users while still allowing publisher branding to appear in other interface elements.
Data Source
AI summary
Techniques are described herein for selecting representative images for video items using a trained machine learning engine. A training set is fed to a machine learning engine. The training set includes, for each image in the training set, input parameter values and an externally-generated score. Once a machine learning model has been generated based on the training set, input parameters for unscored images are fed to the trained machine learning engine. Based on the machine learning model, the trained machine learning engine generates scores for the images. To select a representative image for a particular video item, candidate images for that particular video item may be ranked based on their scores, and the candidate image with the top score may be selected as the representative image for the video item.