Object Detection With Bayesian Uncertainty for Unfamiliar Images
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Solution Overview
Problem
Existing image and video analysis technologies using machine learning algorithms struggle with accurately identifying objects of interest in unfamiliar images or videos, particularly when similar features exist, and fail to account for uncertainty in predictions, leading to inaccurate categorization.
Innovation Solution
A computer-based method utilizing deep neural networks (DNNs) with Bayesian analysis and Monte-Carlo Sampling to generate feature maps, incorporating prior knowledge and addressing uncertainty, thereby improving object detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning algorithms are used for object detection, then the system can process images and videos, but it fails to accurately identify objects in unfamiliar images and cannot account for uncertainty in predictions
Solution Approach 1:
The patent transforms the deterministic output of traditional machine learning models into probabilistic predictions by changing the output parameter from a single category label to a distribution over possible categories. This is achieved through temperature scaling and softmax function modifications that allow the model to express uncertainty and adapt to unfamiliar inputs while maintaining reliability in predictions.
2Measurement precision
If machine learning algorithms are trained with large datasets, then they can recognize common objects, but they mistake similar features for objects of interest when encountering unfamiliar objects
Solution Approach 1:
The patent implements a feedback mechanism where the model's confidence predictions are continuously adjusted through temperature scaling. When the model encounters unfamiliar objects with similar features, the temperature scaling modifies the softmax output to reflect appropriate uncertainty levels, preventing overconfident misclassifications while maintaining accurate identification of familiar objects.
3Productivity
If categorical analysis results are produced without uncertainty measurement, then the system provides definitive classifications, but it cannot distinguish between confident and uncertain predictions
Solution Approach 1:
The patent adds a new dimension to the classification output by introducing temperature as an additional parameter alongside the category prediction. This dimensional extension allows the system to maintain fast categorical classification while simultaneously providing uncertainty information through the temperature parameter, preventing information loss without sacrificing productivity.
Data Source
AI summary
Described herein are software and systems for analyzing videos and/or images. Software and systems described herein are configured in different embodiments to carry out different types of analyses. For example, in some embodiments, software and systems described herein are configured to locate an object of interest within a video and/or image.