Image Processing Method for Activity-Based Highlight Selection
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Solution Overview
Problem
Conventional electronic devices face challenges in efficiently organizing and selecting images from large collections, requiring users to expend significant time and effort to identify and share relevant photos and videos.
Innovation Solution
An electronic device with an image processing method that analyzes activity types based on image and sensor data, applies filters such as quality, duplicate, and classification filters, and generates highlight content by selecting and processing images according to determined activity types, thereby simplifying the image selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually search through collected photos and videos to select relevant content, then selection accuracy can be maintained, but time consumption and user effort increase significantly
Solution Approach 1:
The system performs automatic image selection and grouping without requiring manual user intervention. The processor autonomously analyzes collected images, determines activity types, applies filtering criteria, and generates highlight content groups, enabling the device to serve itself in organizing photo collections.
Solution Approach 2:
The patent replaces manual mechanical searching and selection operations with automated computational processing. The processor uses algorithmic image analysis, activity type determination, and automated filtering to substitute the mechanical process of manual browsing and selection.
2Quantity of substance
If all collected images are stored and made available for selection, then completeness of content is maintained, but complexity of managing and searching through the collection increases
Solution Approach 1:
The patent segments the large collection of images into smaller, meaningful groups based on activity types. By organizing images into distinct activity-based categories (e.g., running, cycling, walking), the system reduces the perceived complexity while maintaining access to all original content through structured segmentation.
Solution Approach 2:
The system changes the organizational parameter from chronological or random storage to activity-type-based classification. This parameter transformation restructures the image collection according to semantic meaning derived from motion analysis, making management and retrieval more efficient.
3Productivity
If automated image selection is implemented to reduce user effort, then time consumption decreases, but selection accuracy and relevance may deteriorate
Solution Approach 1:
The system incorporates multiple feedback mechanisms including quality filter evaluation, duplicate detection, and activity type verification. The processor continuously refines selection criteria based on image quality metrics and activity consistency, ensuring accurate and relevant highlight content generation through iterative feedback loops.
Solution Approach 2:
The patent introduces intermediate processing stages including activity type determination as a mediator between raw image collection and final selection. This intermediary analysis layer ensures that selected images are not only high-quality but also contextually relevant to the detected activity, maintaining selection accuracy through structured intermediate evaluation.
Data Source
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AI summary
An electronic device is provided to collect a plurality of images. The electronic device may collect the images by taking pictures/videos with a camera or by receiving the images from an external device. The electronic device also includes a processor able to determine an activity type during at least a portion of time when the images were taken by the camera or an activity type of the device that originally generated the images received from the external device. The processor may be able to select at least some of the collected images based on the activity type to generate a highlight content.