Visual Search Stability via Adaptive Thresholds and Latching
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Solution Overview
Problem
Visual search results in mobile devices are often erratic and unreliable due to factors like shaky photography, disparate lighting conditions, and noise in captured images, leading to inconsistent object identification and false positives.
Innovation Solution
The method involves determining image matching distances between captured video frames and a visual search database, using adaptive thresholds and clustering algorithms to stabilize object match results, and implementing a latching mechanism to maintain confident matches on the user interface despite changes in the image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visual search is performed on captured image data, then object identification capability is provided, but results become erratic and unreliable due to shaky photography, lighting conditions, and image noise
Solution Approach 1:
The system performs preliminary actions by determining image matching distances for multiple candidate objects before final identification, and by maintaining a result history buffer that stores previous match results. This preliminary processing allows the system to evaluate multiple possibilities and select the most stable result, reducing erratic behavior caused by shaky photography and lighting variations.
Solution Approach 2:
The system implements feedback mechanisms by comparing current match results with historical results stored in a buffer, and by using adaptive thresholds that adjust based on observed match patterns. This feedback loop enables the system to distinguish between genuine object changes and transient variations caused by noise or movement, thereby improving result reliability.
2Measurement precision
If multiple iterative analyses are performed to improve object identification, then identification thoroughness increases, but results become more erratic and unreliable
Solution Approach 1:
The system performs preliminary actions by determining image matching distances for multiple candidate objects before final identification, and by maintaining a result history buffer that stores previous match results. This preliminary processing allows the system to evaluate multiple possibilities and select the most stable result, reducing erratic behavior caused by shaky photography and lighting variations.
Solution Approach 2:
The system changes parameters by using adaptive thresholds that adjust based on observed match patterns and by varying the number of results maintained in the history buffer. These parameter adjustments allow the system to optimize between thoroughness and consistency based on current image quality and match stability, preventing over-analysis from causing erratic results.
3Reliability
If adaptive thresholds and clustering algorithms are used to stabilize results, then result stability improves, but computational complexity increases
Solution Approach 1:
The system segments the visual search process into distinct stages: initial matching distance determination, candidate filtering using adaptive thresholds, clustering of similar matches, and result selection from the history buffer. This segmentation allows each component to be optimized independently, managing computational complexity while maintaining stability improvements.
Solution Approach 2:
The system extracts only the most relevant match results by using adaptive thresholds to filter candidates and by maintaining a limited history buffer of significant matches. This extraction approach focuses computational resources on the most promising candidates rather than processing all possible matches, reducing overall complexity while improving stability.
4Reliability
If a latching mechanism is implemented to maintain confident matches, then user interface stability improves, but response time to detect actual object changes may increase
Solution Approach 1:
The latching mechanism is dynamic rather than static - it maintains matches based on confidence levels and historical consistency rather than simply holding the first result. The system adapts the latch duration and threshold based on match quality, allowing faster updates when confidence is high and more stable maintenance when confidence is lower, thus balancing stability with responsiveness.
Solution Approach 2:
The system changes parameters by adjusting the confidence thresholds and history buffer size based on observed match patterns. When matches are consistently high-quality, the system can use higher thresholds and longer latch durations. When variability increases, parameters are adjusted to detect changes more quickly, maintaining the balance between stability and response speed.
Data Source
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AI summary
Various methods for visual search stability are provided. One example method includes determining a plurality of image matching distances for a captured object depicted in a video frame, where each image matching distance being indicative of a quality of a match between the captured object and a respective object match result. The example method further includes including, in a candidate pool, an indication of the object match results having image matching distances in a candidate region, discarding the object match results having image matching distances in a non-candidate region, and analyzing the object match results with image matching distances in a potential candidate region to include, in the candidate pool, indications of select object match results with image matching distances in the potential candidate region. Similar and related example methods and example apparatuses are also provided.