Context-Weighted Image Repository Filtering for Efficient Object Analysis
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Solution Overview
Problem
Existing computer vision techniques fail to consider the context in which objects appear, leading to inefficient use of computing resources due to the need for separate classification and detection models on every image in a repository.
Innovation Solution
A filtering approach is employed to remove images lacking objects of interest, followed by context-based evaluation using context-specific weights to determine a context-weighted metric, optimizing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate classification and detection models are applied to every image in the repository, then object detection accuracy is improved, but computational overhead and resource consumption increase
Solution Approach 1:
The patent applies preliminary filtering to identify and remove images that do not contain objects of interest before applying the full classification and detection pipeline. This preliminary action reduces the number of images requiring computationally intensive processing, thereby lowering overall computational overhead while maintaining detection accuracy for relevant images
Solution Approach 2:
The patent extracts and removes irrelevant images from the processing pipeline through filtering mechanisms. By taking out images that do not contain objects of interest, the system avoids wasting computational resources on unnecessary processing while preserving the accuracy of object detection for images that actually contain the target objects
2Loss of information
If all images in the repository are processed through classification and detection, then comprehensive object analysis is achieved, but resource consumption increases
Solution Approach 1:
The system performs preliminary filtering to identify images containing objects of interest before applying resource-intensive classification and detection models. This ensures that comprehensive analysis is applied only to relevant images, maintaining information completeness for target objects while reducing overall energy consumption
Solution Approach 2:
The patent applies the full classification and detection pipeline selectively to only those images that pass the filtering stage and contain objects of interest. This partial application of the complete analysis pipeline ensures comprehensive object analysis for relevant images while avoiding excessive resource consumption on irrelevant images
3Measurement precision
If context-based evaluation with multiple subclassifications is implemented, then object classification precision is improved, but processing complexity increases
Solution Approach 1:
The patent segments the object classification process into multiple hierarchical levels, including parent classifications and subclassifications. This segmentation allows the system to first determine broader categories and then apply more specific classification only when needed, improving classification precision while managing processing complexity through structured decomposition
Solution Approach 2:
The patent applies different levels of classification detail to different images based on their content. By determining context-specific weights and applying context-based evaluation only where relevant, the system achieves high classification precision for objects that require it while avoiding unnecessary complex processing for images where simpler classification suffices
Data Source
AI summary
This document relates to context-based evaluations of objects in an image repository. For example, the disclosed implementations can employ image classification and/or object detection models to filter images in an image repository to obtain a filtered subset of images. Then, a context-weighted metric can be determined for a designated object subclassification of objects in the subset. For instance, the context-weighted metric can represent the significance of the designated subclassification relative to one or more other object subclassifications from a larger object classification. The context-weighted metric can be employed for operations such as responding to queries over the image repository.