Multimedia Object Detection Confidence Classification
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Solution Overview
Problem
Existing solutions for object detection in multimedia files are inadequate for accurately identifying objects present within the files, leading to difficulties in generating effective multimedia search engines that can efficiently access and provide relevant information.
Innovation Solution
The method involves identifying independently separable aspects of a multimedia file and using an object detection model to classify objects based on confidence levels, generating a multimedia search engine that leverages these classifications to provide accurate and informative access to multimedia content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If object detection models are used to identify objects in multimedia files, then object identification capability is improved, but measurement precision and reliability of object presence determination deteriorate due to insufficient confidence level differentiation
Solution Approach 1:
The patent introduces a confidence level parameter to transform the binary object detection output into a probabilistic measure. By changing the output parameter from simple presence/absence to confidence level (0-1 scale), the system enables more precise determination of object presence. Objects are classified as present when confidence exceeds threshold 0.5, providing a mathematically sound precision improvement.
Solution Approach 2:
The confidence level acts as an intermediary between the object detection model and the final object presence determination. This intermediary layer allows for nuanced interpretation of detection results, enabling the system to distinguish between certain and uncertain detections, thereby improving measurement precision without sacrificing detection capability.
2Loss of information
If comprehensive object identification is performed across all independently separable aspects of multimedia files, then information completeness is improved, but device complexity and processing requirements worsen
Solution Approach 1:
The patent segments the multimedia file into independently separable aspects (video, audio, text, images) and applies object detection to each aspect separately. This segmentation allows the system to process each modality independently using appropriate models, reducing overall system complexity while maintaining comprehensive information extraction across all aspects.
Solution Approach 2:
The system employs a universal object detection framework that works across multiple independently separable aspects of multimedia files. By designing a multi-functional detection system that can handle video, audio, text, and images through unified confidence level assessment, the patent achieves information completeness without proportionally increasing device complexity.
3Reliability
If confidence level thresholding is applied to classify objects as confident or not confident, then decision reliability is improved, but loss of time for manual verification of uncertain objects worsens
Solution Approach 1:
The patent transforms qualitative judgment decisions into a quantitative parameter-based classification system. By changing the decision parameter from subjective assessment to objective confidence level thresholding (0.5 threshold), the system improves decision reliability while automating what would otherwise require time-consuming manual verification.
Data Source
AI summary
A multidimensional system for generating a multimedia search engine is provided. A computer device identifies a plurality of independently separable aspects of a multimedia file. The computing device provides at least one independently separable aspect of the plurality of independently separable aspects as input into an object detection model. The computing device receives, from the object detection model, an identification of at least one object and a corresponding level of confidence that the object is present in the multimedia file. The computing device classifies the object as either confident or not confident, based on whether the level of confidence meets a threshold level of confidence. The computing device generates a multimedia search engine based, at least in part, on the object and the classification.