Uncertainty Measure for Mixture-Model Pattern Classifiers
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Solution Overview
Problem
Mixture-model based parametric classifiers face challenges in determining the uncertainty of classification decisions, especially on short input sequences, where the finite length of the sequence and non-stationary statistics complicate the assessment of confidence in classification outcomes.
Innovation Solution
A method is introduced to determine an uncertainty measure for mixture-model based parametric classifiers by obtaining a short-term frequency representation of multimedia signals, classifying them using a trained parametric classifier, and calculating the uncertainty based on the relation between posterior probabilities of the input sequence and the training sequence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a mixture-model based parametric classifier is used for classification, then classification accuracy is improved, but the ability to determine uncertainty of classification decisions deteriorates
Solution Approach 1:
The patent segments the classification process into two distinct outputs: (1) the standard classification decision based on maximum a posteriori probability, and (2) a separate uncertainty measure computed by comparing input sequence statistics against training sequence statistics. This segmentation allows the system to maintain high classification accuracy while simultaneously providing uncertainty information that was previously lost.
Solution Approach 2:
The patent introduces an intermediary mechanism that compares the statistical properties of the input sequence against the training sequence to generate an uncertainty measure. This intermediary comparison process bridges the gap between the classifier's confident decision and the actual reliability of that decision, especially for short sequences where statistics may be non-stationary.
2Productivity
If the length of input sequence is short, then processing speed is improved, but the reliability of classification decision deteriorates
Solution Approach 1:
The patent performs preliminary action by computing and storing the statistics of the training sequence before actual classification occurs. This pre-computed statistical baseline enables rapid uncertainty assessment during classification without requiring long input sequences, thus maintaining both processing speed and reliability even for short sequences.
Solution Approach 2:
The patent implements a feedback mechanism where the uncertainty measure, derived from comparing input sequence statistics with training sequence statistics, feeds back into the classification system. This feedback allows the system to adjust its confidence in classification decisions based on the quality and length of the input sequence, maintaining reliability without sacrificing processing speed.
3Adaptability or versatility
If statistics are non-stationary, then adaptability to changing conditions is improved, but the accuracy of uncertainty assessment deteriorates
Solution Approach 1:
The patent embraces the dynamic nature of non-stationary statistics by designing an uncertainty assessment mechanism that adapts to changing conditions. Rather than assuming stationary statistics, the system dynamically compares the current input sequence statistics against the training sequence statistics, allowing it to accurately assess uncertainty even when statistics change over time.
Data Source
AI summary
There are provided mechanisms for determining an uncertainty measure of a mixture-model based parametric classifier. A method is performed by a classification device. The method includes obtaining a short-term frequency representation of a multimedia signal. The short-term frequency representation defines an input sequence. The method includes classifying the input sequence to belong to one class of at least two available classes using the parametric classifier. The parametric classifier has been trained with a training sequence. The method includes determining an uncertainty measure of the classified input sequence based on a relation between posterior probabilities of the input sequence and posterior probabilities of the training sequence.