Visual Theme Clustering in Media Collections
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Solution Overview
Problem
Users face difficulty in organizing and viewing large collections of photos and videos, as existing methods require manual identification of similar images or categories, leading to a lack of visual theme identification and cohesive clustering.
Innovation Solution
A computer-implemented method that determines clusters of media items with visual similarity using a machine-learning model, generating vector representations of media items and selecting subsets based on threshold visual similarity values, while excluding prohibited categories and incorporating user feedback to refine clustering and display a user interface showcasing these clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual identification of similar images is used, then user control over categorization is improved, but time consumption and operational complexity increase
Solution Approach 1:
The system performs automatic visual theme identification and media clustering without requiring manual user intervention. The machine learning model independently analyzes media items, extracts visual features, and organizes them into themed clusters, allowing the system to serve itself rather than relying on continuous user input.
Solution Approach 2:
The patent replaces manual mechanical categorization operations with automated computational processes. Instead of users manually sorting media items, the system uses machine learning models to automatically identify visual themes and cluster media, substituting human manual work with algorithmic processing.
2Productivity
If automatic clustering is implemented, then time consumption is reduced, but precision of visual theme identification may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where user interactions with automatically generated clusters are used to refine and improve the clustering algorithm. User feedback on cluster relevance and visual theme accuracy allows the model to learn and adjust, progressively improving identification precision while maintaining automated processing speed.
Solution Approach 2:
The system performs preliminary extraction of visual features and pre-processing of media items before clustering. By preparing data in advance and pre-computing visual characteristics, the system enables faster subsequent clustering operations without sacrificing identification accuracy, as the heavy lifting of feature extraction is done beforehand.
3Quantity of substance
If all media items are clustered, then completeness of organization is improved, but complexity of the system increases
Solution Approach 1:
The system segments the large collection of media items into smaller, manageable visual theme clusters. Instead of treating all media as a single homogeneous group, it divides them into distinct themed categories (e.g., landscapes, portraits, events), reducing the complexity of managing the entire collection by breaking it into organized segments.
Solution Approach 2:
The system extracts and removes prohibited or irrelevant categories from the media collection before clustering. By filtering out unwanted content and focusing only on relevant media items, the system reduces the effective complexity of the clustering task while maintaining completeness of the desired organization.
4Quantity of substance
If visual similarity threshold is lowered, then number of clusters increases improving coverage, but coherence of each cluster deteriorates
Solution Approach 1:
The system employs dynamic threshold adjustment based on the characteristics of the media collection and user preferences. Rather than using a fixed similarity threshold, the system adapts the threshold dynamically to balance cluster coherence and coverage, allowing the clustering behavior to change based on the specific data being processed.
Solution Approach 2:
The system changes the visual similarity threshold parameter based on the identified visual themes and distribution of media items. By adjusting this parameter according to the specific clustering context, the system optimizes the balance between creating enough clusters for comprehensive coverage while maintaining sufficient coherence within each cluster.
Data Source
AI summary
A media application determines, based on pixels of images or videos from a collection of media items, clusters of media items such that the media items in each cluster have a visual similarity, wherein the collection of media items is associated with a user account. The media application selects a subset of the clusters of media from corresponding clusters of media items based on the media items in each cluster having a visual similarity within a range of threshold similarity values. The media application causes a user interface to be displayed that includes the subset of the clusters of media.