Neural Network Image Selection for Text Relevance
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Solution Overview
Problem
The vast number of images available from photo stock companies makes it difficult for producers of multimedia content to select the most appropriate images for their projects, as existing search methods often rely on simple keyword searches that do not accurately match the subject matter of the text.
Innovation Solution
A machine learning system is trained to analyze text and select images based on attribute vectors associated with images, using a neural network that can process significant sections of text, such as sentences or paragraphs, to improve the relevance of search results, and an automated image selection system that includes image tagging and tracking logic to refine the image selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a simple keyword search is used to find images, then the search process is fast and easy to implement, but the image selection accuracy and relevance to the text subject matter deteriorates
Solution Approach 1:
The patent segments the text into multiple sections (title, headings, paragraphs, sentences) and processes each section separately to generate multiple attribute vectors. This allows the system to capture different aspects of the text content and combine them for more accurate image matching, resolving the contradiction between search efficiency and accuracy by maintaining a structured approach that can be scaled.
Solution Approach 2:
The patent transitions from simple keyword matching to a multi-dimensional attribute vector approach. Instead of searching based on single keywords, the system generates attribute vectors that represent multiple dimensions of text content (semantic meaning, context, relationships). This dimensional expansion enables more precise image selection while maintaining searchability through the structured vector representation.
2Quantity of substance
If the vast number of available images is searched using traditional methods, then more image options are available, but the difficulty of selecting the most appropriate image increases
Solution Approach 1:
The patent implements a feedback mechanism where the system generates attribute vectors from text, compares them against image attribute vectors, and ranks images based on matching quality. This automated feedback loop reduces selection complexity by objectively evaluating image-text compatibility rather than relying on manual assessment of numerous options, allowing the system to maintain large image inventories while simplifying the selection process through algorithmic evaluation.
Solution Approach 2:
The system performs self-service by automatically analyzing text content, generating appropriate attribute vectors, and identifying matching images without requiring manual intervention for each selection. The automated image selection system processes text sections, generates vectors, and retrieves relevant images autonomously, reducing the complexity burden on users while maintaining access to large image collections.
3Ease of manufacture
If keyword-based image search is used, then the implementation is simple, but the relevance of search results to the actual subject matter of the text deteriorates
Solution Approach 1:
The patent replaces the mechanical keyword-matching system with an automated attribute vector generation and comparison system. Instead of relying on simple text-string matching, the system uses computational algorithms to generate semantic representations of text sections and compare them with image attributes. This substitution maintains implementation feasibility through automated processing while significantly improving result reliability by capturing semantic meaning rather than just keyword presence.
Data Source
AI summary
Published multimedia including both images and associated text are used to train a neural network, or other machine learning system. The neural network is trained to facilitate the identification and selection of other images for association with other text, and subsequent publishing together in multimedia. The neural network is optionally configured to receive text, or a representation thereof, and generate an image feature vector in response. Embodiments include the use of the trained neural network to select images for publication in multimedia.