Multimedia Signature Deviation Detection
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Solution Overview
Problem
Existing multimedia content analysis systems face challenges in detecting deviations from common patterns due to the even distribution of patterns within data, requiring extensive computing resources, and struggle with identifying irregular events effectively.
Innovation Solution
A method and system that generate signatures for multimedia content segments, compare consecutive segments to detect periodic behavior patterns, and identify deviations by comparing subsequent segments to a baseline signature, generating notifications upon deviation detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pattern recognition techniques are used to detect uncommon patterns in multimedia content, then detection capability is improved, but computing resources are excessively consumed
Solution Approach 1:
The system performs preliminary actions by generating signatures for multimedia content segments and establishing periodic behavior patterns in advance. This allows the system to pre-process and organize data into reusable signature templates, so that when detection is needed, it only needs to compare against pre-established patterns rather than analyzing raw data from scratch, significantly reducing real-time computing resource consumption.
Solution Approach 2:
The system creates simplified copies of multimedia content in the form of signatures that capture essential periodic behavior characteristics. These signature copies are much more compact and computationally efficient to process than the original multimedia content, allowing rapid comparison and detection while maintaining detection accuracy. The signature acts as a lightweight representation that preserves the essential pattern information.
2Measurement precision
If extensive computing resources are allocated to recognize rare patterns, then detection accuracy is improved, but system efficiency deteriorates
Solution Approach 1:
The system pre-establishes periodic behavior patterns and generates signatures for common patterns in advance, creating a library of expected behaviors. This preliminary organization allows the system to quickly compare incoming content against pre-defined patterns rather than performing exhaustive analysis, maintaining high detection accuracy for rare events while dramatically improving overall system efficiency through efficient resource allocation.
Solution Approach 2:
The system applies different processing qualities to different parts of the data. Common, frequent patterns receive simplified signature-based processing for high efficiency, while rare, uncommon patterns trigger more detailed analysis only when needed. This local differentiation of processing quality ensures that computational resources are concentrated where they are most needed (detecting rare anomalies) while maintaining high efficiency for routine processing.
3Measurement precision
If pattern distribution is even throughout data, then comprehensive coverage is improved, but recognition complexity increases
Solution Approach 1:
The system segments multimedia content into discrete segments and generates signatures for each segment. This segmentation breaks down the complex task of analyzing entire multimedia files into manageable piecewise units. Each segment can be independently analyzed and compared against periodic patterns, making the recognition process less complex while maintaining comprehensive coverage through systematic segment-by-segment processing.
Solution Approach 2:
The system creates signature copies that represent essential characteristics of each segmented portion of multimedia content. These signatures serve as simplified proxies that capture the essential periodic behavior without requiring analysis of the complete original data. This copying approach reduces recognition complexity by working with compact signature representations rather than full multimedia segments, while maintaining comprehensive coverage through systematic signature generation and comparison.
Data Source
AI summary
A method and system for identification of a deviation from a periodic behavior pattern in a sequence of multimedia content segments are provided. The system comprises receiving the sequence of multimedia content segments; generating at least one signature for each multimedia content segment of the sequence of multimedia content segments; comparing at least two signatures generated for at least two consecutive multimedia content segments to detect a periodic behavior pattern; upon detecting the periodic behavior pattern, comparing at least one signature generated for at least a subsequently received multimedia content segment to at least one signature representing the detected multimedia content segment to identify a deviation from the periodic behavior pattern; and upon identifying the deviation from the periodic behavior pattern, generating a notification with respect to the at least one deviation.