Image Stacking via Time Proximity and Visual Similarity Analysis
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Solution Overview
Problem
Conventional methods for stacking images are inefficient, requiring manual selection and organization, and often group disparate images based on time proximity or visual similarity, leading to clutter and unmanageability.
Innovation Solution
A system and method for stacking images that utilizes a visual similarity engine and time analyzer to automatically group images based on both time proximity and visual similarity, allowing for the creation of stacks represented by a single thumbnail, with options for manual modification and automatic selection of the stack representative.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If images are grouped by time proximity, then images captured during a selected time interval are grouped together, but images with no subject matter commonality are grouped together
Solution Approach 1:
The patent segments the image grouping process into two distinct analysis dimensions: time proximity analysis and visual similarity analysis. By dividing the single grouping operation into separate temporal and visual components, the system can evaluate both criteria independently and combine them to achieve accurate grouping without sacrificing precision for speed.
Solution Approach 2:
The patent introduces an intermediary grouping mechanism that acts as a mediator between time-based and visual-based grouping approaches. This intermediary layer combines results from both analysis methods, allowing the system to leverage the speed advantage of time proximity while maintaining the accuracy of visual similarity through a composite grouping decision.
2Manufacturing precision
If images are grouped by visual similarity, then images with common scenery are grouped together, but differing views of the same subject captured years apart are grouped together
Solution Approach 1:
The patent segments the grouping criteria into separate evaluable components by analyzing visual similarity and time proximity independently. This segmentation allows the system to apply appropriate weighting or thresholds to each criterion, preventing visual similarity alone from causing inaccurateč·¨-year groupings while maintaining its precision benefits.
Solution Approach 2:
The system employs an intermediary grouping layer that reconciles visual similarity results with time proximity data. This intermediary mechanism prevents over-grouping by filtering visual matches through temporal context, ensuring that images from different time periods are not incorrectly grouped together despite visual similarities.
3Manufacturing precision
If manual selection of images for stacking is required, then users can precisely control which images are grouped, but the process becomes tedious and time consuming
Solution Approach 1:
The patent performs preliminary automatic grouping actions based on time proximity and visual similarity analysis before final user confirmation. By pre-processing the grouping task with automated algorithms that evaluate both temporal and visual criteria, the system reduces the manual effort required while maintaining user control over the final grouping decisions.
Solution Approach 2:
The system enables self-service image grouping by automatically analyzing time proximity and visual similarity to generate suggested stacks. Users can review and confirm these automatically generated groupings, which significantly reduces manual intervention while preserving precision through user oversight of the AI-generated groupings.
4Reliability
If multiple images are captured in burst mode, then the chance of capturing a usable image increases, but image window clutter and unmanageability increase
Solution Approach 1:
The patent merges multiple individual image files into a single stack object that is represented by one thumbnail in the interface. By combining burst-mode images into unified stacks based on temporal and visual criteria, the system reduces interface clutter while preserving all captured images within the stack structure, making management simpler without sacrificing image quality options.
Solution Approach 2:
The system implements a nested structure where multiple images are contained within a stack, which itself is represented as a single unit in the user interface. This nesting approach allows burst-mode images to be organized hierarchically, with the stack container holding individual image files, thereby reducing visual complexity while maintaining access to all captured images.
Data Source
AI summary
Automatic stacking based on time proximity and visual similarity is described, including a method, comprising analyzing a time proximity of a plurality of electronic images, performing a visual similarity analysis on the plurality of electronic images, and stacking the plurality of electronic images based on a result of the time proximity analysis and the visual similarity analysis.