Deep Content Tagging Scene-Based Genre Classification
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Solution Overview
Problem
Existing media content systems fail to accurately categorize media items based on their genre, as they are often labeled under a single genre despite containing elements of multiple genres, leading to incomplete search results and user dissatisfaction.
Innovation Solution
A method and apparatus for deep content tagging using neural network models that analyze media content on a scene-by-scene basis, detecting objects and features to identify multiple genres associated with each scene, allowing for dynamic content control and personalized user experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If media content is labeled under a single genre for simplicity, then the labeling process is fast and easy, but the accuracy and completeness of genre classification deteriorates
Solution Approach 1:
The patent segments media content into multiple scenes and analyzes each scene independently to identify genres. Instead of assigning a single genre to the entire media item, the system generates genre labels for individual scenes, allowing a single media content to be associated with multiple genres across different scenes. This segmentation approach resolves the contradiction by enabling comprehensive genre classification without sacrificing labeling efficiency.
2Measurement precision
If neural network models analyze media content scene-by-scene to identify multiple genres, then genre classification accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-processing media content to divide it into scenes before genre classification. This preliminary segmentation organizes the content in a way that facilitates efficient processing by the neural network model. By preparing the content structure in advance, the system reduces the computational burden during the actual genre analysis phase while maintaining high classification accuracy.
3Ease of operation
If traditional single-genre labeling is used, then the system is simple to operate, but user satisfaction and search result quality deteriorate
Solution Approach 1:
The patent implements multi-functionality by enabling the media content system to serve multiple genre classification purposes simultaneously. The same neural network model and scene analysis mechanism support both detailed genre identification for search optimization and simplified genre presentation for user interface. This universal approach allows the system to maintain operational simplicity while delivering reliable, multi-genre search results that improve user satisfaction.
Data Source
AI summary
A method and apparatus for deep content tagging. A media device receives one or more first frames of a content item, where the one or more first frames spans a duration of a scene in the content item. The media device detects one or more objects or features in each of the first frames using a neural network model and identifies one or more first genres associated with the first frames based at least in part on the detected objects or features in each of the first frames. The media device further controls playback of the content item based at least in part on the identified first genres.