Video Preference Tracking via Frame Feature Segmentation
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Solution Overview
Problem
Existing video preference determination technologies cannot track viewer preferences over continuous content viewing or changes in preferences.
Innovation Solution
A video processing system that extracts frame feature values to characterize each frame, stores them in correspondence to viewers, and groups them by attributes, comparing these values to determine scene groups preferred by viewers based on interest levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frame feature values are stored and compared for each individual scene, then preference determination precision is improved, but the ability to track continuous viewing preferences and preference changes deteriorates
Solution Approach 1:
The patent segments the video content into multiple scenes, and further segments the preference determination into multiple dimensions: individual scene preference, continuous viewing preference, and preference change tracking. Each segment is handled by specific components (frame feature value extraction means, preference determination means, continuous viewing preference determination means) that work together to provide comprehensive preference analysis.
Solution Approach 2:
The patent adds temporal continuity as a new dimension to the preference determination process. By introducing continuous viewing preference determination that tracks preference across multiple scenes and time, the system transforms from static single-scene analysis to dynamic multi-dimensional preference tracking, enabling both precision and adaptability.
2Measurement precision
If detailed frame feature values are extracted and stored for preference analysis, then preference determination accuracy is improved, but system complexity and data storage requirements worsen
Solution Approach 1:
The patent extracts only the essential frame feature values needed for preference determination, rather than processing all video data. The frame feature value extraction means selectively extracts relevant features from video frames, and the preference determination means processes only these extracted features, reducing computational complexity while maintaining accuracy.
Solution Approach 2:
The system performs preliminary extraction and organization of frame feature values before preference determination. By pre-processing the video content to extract and store frame feature values in an organized manner (grouped by scenes and attributes), the system reduces the complexity of subsequent preference analysis operations.
Data Source
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AI summary
To provide a video processing apparatus can determine preference of a viewer from contents what the viewer has continued to view and track a change of the preference of the viewer. The video processing apparatus includes a first storage means which stores, in correspondence to a viewer, frame feature values to characterize each frame of scenes constituted by a series of frames in a video content viewed by the viewer; a second storage means which stores, as scene groups classified by attributes of the scenes, the frame feature values of scenes constitute by the series of frames; an interest level accumulation means which compares the frame feature values stored in the first storage means with the compared frame feature values stored in the second storage means, and in case the compared frame feature values match, increases a score about the viewer which represents the interest level with respect to the scene group of which the frame feature values match; and a viewer preference determination means which determines that the scene groups of the which the scores are higher are the scene groups preferred by the viewer.