Multimodal Video Classification via Scene Segmentation
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Solution Overview
Problem
Current video classification methods are inefficient in categorizing video streams due to reliance on single modalities, extensive processing resources, and inability to grasp the contextual essence of video content, often leading to incorrect categorization and high processing times.
Innovation Solution
A staged classification approach that divides video streams into scenes, extracts visual, audio, and textual data, applies classification functions to calculate low-level concept probabilities, and aggregates these to categorize scenes and streams into high-level semantic categories, reducing processing requirements and improving precision by utilizing multimodal analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single modality classification is used, then device complexity is reduced, but measurement precision and reliability of video categorization deteriorate
Solution Approach 1:
The patent combines multiple modalities (visual, audio, textual) into a unified classification framework. Each modality is processed through its own classification function, and the results are integrated to produce a comprehensive scene category probability, thereby improving categorization precision without requiring a single overly complex system.
Solution Approach 2:
The classification system is segmented into separate classification functions for each modality (visual, audio, textual). This allows each function to specialize in processing its specific modality type, improving overall precision while keeping individual components manageable in complexity.
2Measurement precision
If extensive processing is applied to all video data, then measurement precision improves, but productivity and use of energy deteriorate
Solution Approach 1:
The video stream is segmented into discrete scenes, and classification is applied scene-by-scene rather than to the entire video stream. This reduces the computational burden per processing unit while maintaining precision through focused analysis of each scene's multimodal data.
Solution Approach 2:
The system processes only the necessary modality data for each scene to achieve accurate categorization, rather than exhaustively processing all possible data. The classification functions process data to the extent needed to generate reliable scene category probabilities, avoiding unnecessary computational overhead.
3Measurement precision
If extensive processing is applied to all video data, then measurement precision improves, but loss of time increases
Solution Approach 1:
By dividing the video stream into scenes and processing each scene independently through parallel classification functions, the system reduces overall processing time while maintaining precision. Each scene is categorized based on its specific multimodal data without waiting for complete analysis of the entire video stream.
4Ease of operation
If single modality analysis is used, then ease of operation is improved, but reliability of contextual understanding deteriorates
Solution Approach 1:
The patent merges multiple modality analyses (visual, audio, textual) to achieve reliable contextual understanding. Each modality provides complementary information that, when combined, creates a more accurate and reliable interpretation of video content context than any single modality could provide alone.
Data Source
AI summary
A computer implemented method of semantically categorizing a video stream through multimodal content classification, comprising dividing a designated video stream to a plurality of scenes by analyzing a visual content of a plurality of frames of the video stream to identify scene changes between consecutive scenes, applying a plurality of classification functions to each of a plurality of modalities extracted from each of the scenes to calculate a class probability for each of a plurality of known concepts detected in each scene, applying a plurality of multimodal classification functions on the class probability of the known concepts to calculate a scene category probability for each scene indicating a probability of the scene to be categorized in one or more semantic categories and categorizing the video stream to a stream category of the semantic categories by aggregating the category probability of the scenes.