Video Shot Classification Using Scene Score Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image classification systems are inadequate for classifying videos due to their inability to handle variations and quality issues in video frames, such as blurry or occluded images, and are insufficient for identifying diverse content types, leading to inconsistent results and inefficiencies in content-based video indexing and retrieval.
Innovation Solution
A system that processes video files by extracting frames, discarding dark frames, and generating scene classification score vectors using classifiers to determine the presence of predefined scene categories, with features like material classification and arrangement vectors to accurately classify video content, enabling efficient indexing and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image classification systems are used to classify video frames, then the system structure remains simple, but the classification accuracy deteriorates due to inability to handle frame variations and quality issues
Solution Approach 1:
The video frame is divided into multiple grid cells to enable localized analysis. Each grid cell can be independently processed to handle variations in content, allowing the system to accurately classify different regions even when parts of the frame are blurry or occluded. This segmentation approach improves overall classification accuracy without requiring complete frame analysis.
Solution Approach 2:
Different grid cells within the same frame are analyzed with different levels of detail based on their content characteristics. The system identifies and processes regions with higher quality (clearer, less occluded) frames more extensively, while applying simpler processing to lower quality regions. This selective processing strategy maintains high classification accuracy while managing computational complexity.
2Productivity
If all video frames are processed for classification, then comprehensive content analysis is achieved, but processing time increases due to large number of frames
Solution Approach 1:
Instead of processing every frame in the video, the system extracts and processes only a representative subset of frames. By strategically selecting key frames that capture the essential content characteristics, the system achieves comprehensive content analysis with significantly reduced processing time. This partial processing approach maintains indexing efficiency while avoiding the time cost of full frame analysis.
3Adaptability or versatility
If simple classification categories are used, then the classification system remains easy to implement, but the ability to identify diverse content types deteriorates
Solution Approach 1:
The classification system transitions from simple single-label categories to multi-dimensional classification by assigning multiple labels to frames based on their content. The grid cell analysis enables the system to identify diverse content types simultaneously (e.g., both 'outdoor' and 'natural scene' labels), adding dimensional complexity to the classification structure. This allows comprehensive content type identification while maintaining implementability through systematic multi-label assignment procedures.
Data Source
AI summary
The present disclosure relates to systems and methods for classifying videos based on video content. For a given video file including a plurality of frames, a subset of frames is extracted for processing. Frames that are too dark, blurry, or otherwise poor classification candidates are discarded from the subset. Generally, material classification scores that describe type of material content likely included in each frame are calculated for the remaining frames in the subset. The material classification scores are used to generate material arrangement vectors that represent the spatial arrangement of material content in each frame. The material arrangement vectors are subsequently classified to generate a scene classification score vector for each frame. The scene classification results are averaged (or otherwise processed) across all frames in the subset to associate the video file with one or more predefined scene categories related to overall types of scene content of the video file.