Visual Search Probability Distribution Update for Stability
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Solution Overview
Problem
Conventional visual search classifiers produce unstable and computationally intensive classification results, leading to sluggish user experiences and apparent instabilities due to vacillation near probability distribution thresholds, especially in coarse object detection.
Innovation Solution
Updating probability distributions based on successive frames until a specified condition is met, and producing search results only after the condition is satisfied, using a backend server to increment parameters of beta distributions for binomial conjugate priors, and refining object classes before sending digital supplements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional visual search classifiers process each image independently, then classification speed is maintained, but search results become unstable and vacillate near probability thresholds
Solution Approach 1:
The system performs preliminary classification on the first image to obtain an initial probability distribution, then uses this as a baseline for comparison with subsequent images. This preliminary action allows the system to avoid re-processing from scratch while maintaining stability by comparing against a established reference point.
Solution Approach 2:
Instead of independent classification of each image, the system continuously updates the probability distribution by comparing successive images. The classification process becomes continuous rather than discrete, maintaining useful action across multiple images to stabilize results near thresholds.
2Productivity
If visual search performs coarse classification first, then computational complexity is reduced, but precision of object identification decreases
Solution Approach 1:
The classification process is segmented into two distinct stages: coarse classification to identify broad object categories, and fine classification to determine specific object classes. This segmentation allows the system to first reduce computational complexity with coarse classification, then apply more precise classification only when needed.
Solution Approach 2:
The system dynamically adjusts the classification depth based on the results of coarse classification. When the coarse classification probability is high and stable, the system can stop at the coarse level for efficiency. When probabilities are low or vacillating near thresholds, the system dynamically proceeds to fine classification to improve precision.
3Speed
If visual search returns results immediately after first image processing, then response speed is improved, but result accuracy decreases due to probability vacillation
Solution Approach 1:
The system performs periodic classification on successive images rather than returning immediately after the first image. By sampling at periodic intervals and comparing results, the system can detect when probabilities have stabilized, balancing response speed with result reliability.
Solution Approach 2:
The system uses feedback from comparing probability distributions across successive images to determine when to return results. The feedback mechanism monitors whether classifications are stable or vacillating, and only returns results when stability criteria are met, ensuring both speed and accuracy.
Data Source
AI summary
Techniques of performing a visual search include updating probability distributions based on a succession of frames containing object images until a specified condition has been satisfied and producing a search result for the object only after the specified condition has been satisfied. When a user captures an image of a scene using a device, a front-end, visual search application running on the device obtains successive image frames and sends a first image frame to a back-end computer configured to perform a classification on the frame. The back-end computer obtains a prior probability distribution and generates a likelihood function indicating whether the image frame includes an object. The back-end computer then updates the prior probability distribution by adding respective values of parameters associated with the prior and likelihood function.