Object Recognition Confidence Feedback Loop
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Solution Overview
Problem
Existing image-based search techniques often provide inaccurate information due to issues like hazy or blurry images, partial object views, distant objects, suboptimal angles, and image noise, which hinder accurate object recognition in electronic devices.
Innovation Solution
An electronic device uses machine learning models to detect and classify objects in images, determining a confidence level and requesting user input for additional images or video captures when the confidence level is low, to refine the identification process through guidance on improving image quality and angle adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If machine learning models are used to automatically classify objects in images, then object recognition speed is improved, but recognition accuracy deteriorates when images are hazy, blurry, or taken from suboptimal angles
Solution Approach 1:
The system implements a feedback loop where the machine learning model's confidence level in its classification is evaluated. When confidence is below a threshold, the system requests additional user input (second images or video captures) rather than proceeding with low-confidence results. This feedback mechanism ensures accuracy by only accepting classifications that meet confidence requirements, while maintaining speed through automated processing when confidence is sufficient.
Solution Approach 2:
The system performs partial automated classification using machine learning models for immediate results, then supplements with additional user-captured images or video captures only when necessary (when confidence is low). This partial automation approach balances speed (using ML for quick classifications) with accuracy (adding user input only when needed to resolve uncertain cases).
2Measurement precision
If the system requests additional user input for low confidence classifications, then object recognition accuracy is improved, but user operation complexity increases
Solution Approach 1:
The system uses the device's existing camera and image capture capabilities to obtain additional input images or video captures, leveraging resources already available to the user. The guidance provided to users leverages their existing familiarity with taking photos or recording video, making the additional input process intuitive and self-explanatory rather than requiring complex new interactions.
Solution Approach 2:
The system provides clear feedback to users about why additional input is needed (low confidence classification) and what specific improvements are suggested (image quality, angle adjustments). This feedback guides users on exactly what to do, reducing the perceived complexity by making the requirements transparent and actionable rather than arbitrary.
3Measurement precision
If guidance is provided to users for improving image quality and angle, then object recognition accuracy is improved, but system complexity increases
Solution Approach 1:
The machine learning model acts as an intermediary that automatically analyzes the first image and generates specific, actionable guidance feedback for the user. Rather than requiring complex system-level image processing or multiple sensors, the ML model intermediates between the raw image input and user guidance, translating image quality issues into simple, understandable suggestions about what the user should do differently.
Solution Approach 2:
The system provides guidance that targets specific image parameters (quality, angle, positioning) that users can intuitively adjust. By focusing guidance on these key parameters rather than requiring complex technical adjustments, the system improves accuracy through parameter optimization while keeping the user interface and system complexity manageable.
Data Source
AI summary
The subject technology provides object recognition systems and methods that can be used to identify objects of interest in an image. An image such as live preview may be generated by a display component of the electronic device and an object of interest may be detected in the image. The detected object of interest may be classified using a classification model. Subsequent to classification, a confidence level in identifying the object of interest may be determined. In response to determining that the confidence level does not meet a confidence level threshold for identifying the object of interest, a request for a user input is generated. Based on the user input, the object of interest is identified using an object recognition model.