Neural Network Text-Image Association for Story Albums
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing media organization tools require users to start with a collection of photos and select the best shots, but do not assist in creating a story-based album or book when users provide textual summaries as guidance.
Innovation Solution
A method using a neural network to pair textual summaries with media content, associating summaries with images based on similarity of content features, allowing users to input captions and automatically select relevant images for a coherent media album or book.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users manually select and organize photos using existing tools, then they can create coherent albums with quality control, but the process requires significant user time and effort
Solution Approach 1:
The system enables self-service by automatically selecting and organizing photos based on user-provided textual summaries. The neural network independently performs photo selection, grouping, and sequencing without requiring manual user intervention, thus maintaining album coherence while eliminating time investment from users.
Solution Approach 2:
The patent replaces the mechanical manual sorting and selection process with an automated neural network system. The neural network analyzes textual summaries and automatically matches them with relevant photos, substituting human cognitive and manual labor with an intelligent automated system that preserves selection quality.
2Ease of operation
If existing systems automatically select photos based on content features, then user effort is reduced, but the ability to create story-driven albums with specific narrative guidance is lost
Solution Approach 1:
The system performs preliminary action by requiring users to provide textual summaries that define the desired story or narrative before photo selection occurs. This preliminary textual guidance shapes the subsequent automated photo selection process, ensuring that automation serves the user's storytelling intent rather than operating blindly.
Solution Approach 2:
Textual summaries serve as an intermediary between user intent and automated photo selection. The neural network uses these summaries as a bridge to understand the desired narrative and translate it into appropriate photo selections, maintaining both automation and storytelling capability through this mediating textual layer.
3Adaptability or versatility
If users provide textual summaries as input, then story-driven album creation is enabled, but the system complexity increases
Solution Approach 1:
The neural network performs multiple functions: it processes textual summaries, extracts key themes and entities, matches photos to text, and determines optimal sequencing. This multi-functionality consolidates what could be separate complex modules into a single versatile system that handles the entire story-driven album creation pipeline.
Solution Approach 2:
The system changes parameters by transforming unstructured textual summaries into structured representations that the neural network can process. By converting text into extractable features and themes, the system manages complexity through parameter transformation rather than requiring complex text processing architectures.
Data Source
AI summary
A method and system of associating textual summaries with data representative of media content is provided. The method may include receiving a plurality of textual summaries, each textual summary representative of an event, pairing, by a neural network, each received textual summary with each of a plurality of pieces of data, each piece of data representative of media content, to generate a plurality of text-data pairings; and associating a first selected textual summary with a first piece of data based on a similarity of content features extracted from each received textual summary to content features extracted from each piece of data in each of the plurality of text-data pairings.