Image Collection Labeling via Similarity Clustering
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Solution Overview
Problem
Existing technologies lack effective methods for identifying and categorizing image collections and suggesting relevant images or collections based on user interests, failing to efficiently utilize image similarity and labeling data.
Innovation Solution
The method involves obtaining image similarity data to generate clusters, deriving image and cluster labels with confidence scores, and selecting cluster labels as collection labels, which are used to identify similar images and collections, and provide recommendations to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image collections are manually labeled and categorized, then labeling accuracy can be maintained, but user time and effort are significantly consumed
Solution Approach 1:
The system enables automatic self-labeling of image collections by computing image similarities, clustering images into groups, and generating collection labels without requiring manual user intervention. The computer automatically performs the labeling task that would otherwise require significant user time and effort.
Solution Approach 2:
The manual mechanical process of user-based labeling is replaced with an automated computational system that uses image similarity algorithms, clustering techniques, and automatic label generation to achieve the same labeling function without human time investment.
2Loss of information
If all images in a collection are analyzed individually for labels, then labeling completeness is improved, but computational complexity increases
Solution Approach 1:
The image collection is segmented into multiple clusters based on image similarity. Instead of analyzing all images individually, the system divides them into groups where images within each cluster share similar characteristics, reducing the overall computational complexity while maintaining labeling completeness.
Solution Approach 2:
Images with similar characteristics are merged into the same cluster and assigned the same collection label. This combining approach reduces redundant computation by processing similar images together rather than individually, lowering computational complexity while preserving labeling completeness.
3Measurement precision
If image similarity computation is performed for all image pairs, then clustering accuracy is improved, but processing time increases
Solution Approach 1:
Instead of computing similarity for all possible image pairs (excessive action), the system computes similarities only for images that are likely to be related based on initial features or metadata (partial action). This selective approach maintains clustering accuracy by focusing computation on relevant image pairs while reducing overall processing time.
Data Source
AI summary
Methods, systems, and apparatus for identifying labels for image collections are presented. In one aspect, a method includes obtaining a collection of images; obtaining, for each image in the collection of images, image similarity data that indicates a measure of similarity of the image to other images in the collection of images; generating, based on the similarity data, two or more image clusters from the collection of images, each image cluster including one or more images from the collection of images; for each image cluster: obtaining, for each image in the image cluster, a set of image labels; generating, from each set of image labels obtained for each image in the image cluster, a set of cluster labels; selecting one or more cluster labels from the set of cluster labels; and identifying the selected cluster labels as a set of collection labels for the collection of images.