Image Collection Relevance Ordering for Automated Selection
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Solution Overview
Problem
Users face significant manual effort when selecting representative images from large collections for projects like photo albums or slideshows, as conventional image classification techniques do not adequately reduce the number of images required for inclusion.
Innovation Solution
A computer-implemented system that automatically creates a scalable relevance ordered representation of an image collection by classifying images into clusters based on features like time, actors, and geographic locations, determining relevance measures, and ordering images according to their appeal and similarity, allowing for easy scaling to include only the most relevant images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional image classification techniques are used to navigate through image collections, then users can more easily locate particular images, but users still expend a great deal of manual effort in selecting desired images
Solution Approach 1:
The system performs automatic image selection and ordering itself without requiring user intervention. The processor automatically classifies images into clusters, determines relevance measures, and orders images by relevance, allowing the system to serve itself in the image curation task rather than requiring manual user selection
Solution Approach 2:
The system changes the parameter of image selection from manual user choice to automated relevance-based ordering. By computing relevance measures based on cluster proximity and image features, the system transforms the selection criterion from subjective user preference to objective computational metrics
2Adaptability or versatility
If users manually sort through large image collections to select representative images, then they can choose images for projects, but the process requires significant manual effort and time
Solution Approach 1:
The system performs preliminary classification and relevance assessment of all images before the user needs to select any. By pre-organizing images into clusters and pre-computing relevance measures, the system prepares the image collection in advance so that when users do need images, they are already ordered by relevance and can be selected immediately
Solution Approach 2:
The system replaces the mechanical process of manual image sorting and selection with an automated computational system. Instead of users physically browsing and selecting images, the processor automatically classifies, measures relevance, and orders images based on computational algorithms
3Quantity of substance
If all images from a large collection are included in a photo album or slideshow, then complete coverage is achieved, but the project becomes unwieldy and difficult to manage
Solution Approach 1:
The system extracts only the most relevant images from the complete collection for inclusion in the project. By identifying and removing redundant or less relevant images through cluster-based classification and relevance ordering, the system leaves only the essential images needed for the photo album or slideshow
Solution Approach 2:
The system applies partial action by including only a subset of images rather than all images. By ordering images by relevance and selecting only the top portion needed for the project, the system achieves sufficient coverage without the excess of including every available image
Data Source
AI summary
In a method of automatically creating a scalable relevance ordered representation of an image collection, the images in the image collection are classified into a plurality of clusters based upon a feature of the images. In addition, respective relevance levels of the images contained in each of the plurality of clusters are determined and the images in each of the plurality of clusters are ordered according to the relevance levels. Moreover, the images from the ordered plurality of clusters are arranged according to a predefined arrangement process to create the scalable relevance ordered representation of the image collection.