Image Reproduction Apparatus Event Time Period Clustering
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Solution Overview
Problem
Conventional image reproducing apparatuses require significant time and effort for users to search and reproduce desired image data due to the need to remember exact dates of image capture, leading to inefficient browsing through large collections of image data.
Innovation Solution
An image reproducing apparatus that determines target images based on a reference date by identifying shooting dates with a predetermined number of images taken, grouping images from adjacent dates to create an event time period for easy data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If image files recorded on the same date are put together into one event, then images can be organized by date, but image data not taken on the designated date is exempted from reproduction requiring users to remember exact dates
Solution Approach 1:
The system dynamically determines event time periods based on the distribution of shooting dates rather than using fixed date-based events. It identifies clusters of shooting dates with high image density and adapts the event time period to include these clusters, allowing flexible inclusion of images from adjacent dates when they form significant groups.
Solution Approach 2:
The system changes the parameter for event definition from fixed calendar dates to dynamic time periods based on image shooting date distribution. It uses the density of shooting dates as a parameter to determine whether to expand event time periods to include adjacent dates, transforming rigid date-based organization into flexible distribution-based organization.
2Measurement precision
If users search for image data by remembering exact dates, then precise image retrieval is possible, but significant time and effort are required when the exact date is not remembered
Solution Approach 1:
The system performs self-service by automatically analyzing the shooting date distribution and determining event time periods without user intervention. It autonomously identifies clusters of images and sets appropriate time periods, eliminating the need for users to manually specify exact dates or search parameters.
Solution Approach 2:
The system performs preliminary analysis of shooting date distribution before the user initiates image retrieval. It pre-determines event time periods based on the density and clustering of shooting dates, so that when users want to retrieve images, the system has already prepared the appropriate time period ranges.
3Quantity of substance
If a large amount of image data is recorded by increasing recording medium capacity, then more images can be stored, but searching and reproducing desired image data takes a lot of time and effort
Solution Approach 1:
The system segments the large collection of image data into meaningful event time periods based on shooting date distribution clusters. By dividing the entire image set into discrete time period segments where each segment contains images from a specific cluster of dates, users can quickly navigate to relevant segments without searching through all images.
Solution Approach 2:
The system adds a new dimension for organizing image data by introducing event time periods based on shooting date density distribution. Instead of only using chronological order or file names, it creates a density-based temporal dimension that groups images by clusters of shooting dates, enabling more efficient retrieval.
Data Source
AI summary
An image reproducing apparatus that is capable of easily reproducing image data intended for viewing. A read-out unit reads out a plurality of images recorded in a recording medium. A control unit determines target images among the plurality of images based on a reference date. The control unit specifies a shooting date on which a predetermined number or greater number of images were taken around the reference date and determines images taken on the specified shooting date as the target images.