Image Grouping by Contextual Features and Timestamps
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Solution Overview
Problem
Users face difficulties in organizing and recalling the context of large volumes of captured content, such as images and videos, over time, especially when the details of capture are forgotten.
Innovation Solution
Systems and methods for automatic narrative creation using context awareness, including automatic media grouping by time and location, ordering images by capture time, converting image signs and captions to textual information, determining textual semantics, and associating these with images, as well as creating groups based on time, location, and object matches, with verification through image and face recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually organize captured content, then organization accuracy can be maintained, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary organization actions automatically at the time of capture by extracting metadata (location, time, camera parameters) and creating initial groupings. This preliminary automated organization reduces the need for later manual intervention while maintaining accuracy through subsequent verification steps.
Solution Approach 2:
The system enables self-service organization by automatically analyzing captured images, extracting contextual information, and generating organized groupings without user intervention. The system serves itself by autonomously performing metadata extraction, image analysis, and classification tasks that would otherwise require manual user effort.
2Measurement precision
If users manually recall and organize content details, then contextual accuracy can be achieved, but operational difficulty increases when time has elapsed
Solution Approach 1:
The system captures and stores contextual information (metadata, scene analysis, object detection results) at the moment of image capture, preserving accuracy before user memory fades. This preliminary documentation of context eliminates the need for users to recall details later, maintaining contextual accuracy regardless of time elapsed.
Solution Approach 2:
The system replaces the mechanical process of human memory and manual organization with automated computational processes. Image recognition algorithms, metadata extraction systems, and automated classification replace the need for users to manually recall and organize content, significantly reducing operational difficulty while maintaining accuracy.
3Productivity
If automated grouping is implemented, then time efficiency improves, but system complexity increases
Solution Approach 1:
The automated organization system is segmented into distinct functional modules: metadata extraction module, image analysis module, grouping logic module, and verification module. Each module handles a specific aspect of the organization process, making the overall complex system manageable through modular design and allowing independent optimization of each component.
Solution Approach 2:
The system introduces intermediary components such as metadata structures, intermediate representation formats, and verification layers that mediate between raw captured images and final organized groupings. These intermediaries simplify the complexity by providing structured data formats and verification mechanisms that bridge the gap between automated processing and reliable organization results.
4Measurement precision
If comprehensive analysis of captured images is performed, then narrative accuracy improves, but processing time increases
Solution Approach 1:
The system performs partial analysis by focusing on the most relevant features for narrative generation rather than exhaustive analysis of all image properties. It applies excessive action selectively by performing deep analysis only on key elements (main subjects, critical metadata) while using lighter processing for less important aspects, balancing accuracy with processing efficiency.
Solution Approach 2:
The system applies different levels of analysis quality to different parts of the image and data set. Critical regions (main subjects, text overlays, key metadata) receive comprehensive local quality analysis, while less important areas receive minimal processing. This localized quality approach maintains narrative accuracy for essential elements while reducing overall processing time.
Data Source
AI summary
Systems, devices and methods for automatic narrative creation for captured images. In one example, the system and method perform or include capturing, with an image sensor, a plurality of images having at least one object; creating, with an electronic processor, an initial sequence of images based on a time stamp associated with each image in the plurality of images; identifying, with the electronic processor, textual information within at least one image in the plurality of images; and generating a grouping of the plurality of images based on a criteria selected from a group consisting of a location associated with the plurality of images, textual information within an image in the plurality of images, a search score associated with the at least one object, and a time gap between consecutive images in the initial sequence of the plurality of images.