Neural Network Image Retrieval via Textual Intent
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Solution Overview
Problem
Conventional image retrieval techniques are unable to capture and consider the purpose or intent of a user's search, particularly when expressed in natural language, leading to inaccurate image identification.
Innovation Solution
A machine learning-based system that uses an artificial neural network (ANN) to generate a feature representation of an image and its associated textual description as an associated pair, allowing for improved image search and retrieval by considering both visual and textual features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional image retrieval techniques are used, then image search can be performed, but the system cannot capture and consider the purpose or intent of a user's search expressed in natural language
Solution Approach 1:
The patent merges image retrieval with natural language understanding by integrating a neural network that processes both image data and textual descriptions. The system combines visual feature extraction with text embedding to create a unified representation that captures both the image content and the user's intent expressed in natural language, thereby resolving the contradiction between adaptability to language queries and reliability of image identification.
Solution Approach 2:
The neural network serves as an intermediary between the image repository and the natural language query. It processes the textual description to extract semantic meaning and combines it with image features, acting as a mediator that translates human intent into accurate image retrieval, thus improving both language understanding capability and identification accuracy.
2Ease of operation
If only image properties are matched, then retrieval can be performed, but the purpose or intent of the search is not captured
Solution Approach 1:
The system performs preliminary processing of the textual description by extracting features and generating embeddings before the actual image retrieval. This preliminary action captures the search intent information from the natural language query, preserving it for subsequent matching operations, thereby preventing loss of intent information while maintaining ease of operation.
Solution Approach 2:
The neural network performs multiple functions: it processes the textual description, extracts semantic features, generates embeddings, and uses these for image matching. This multi-functionality allows the system to handle both the ease of natural language operation and the preservation of search intent information, as the same component performs both interpretation and retrieval functions.
3Adaptability or versatility
If conventional techniques are used, then simple image search is possible, but the system is incapable of understanding the meaning of natural language expressions
Solution Approach 1:
The patent replaces conventional mechanical image matching mechanisms with a neural network-based system that processes natural language. Instead of simple pixel-based comparison, the system uses neural networks to understand semantic meaning in natural language expressions, substituting the mechanical retrieval process with an intelligent processing system that handles language complexity.
Solution Approach 2:
The system changes the parameters of image representation by transforming them into neural network embeddings that capture semantic meaning. By changing from traditional image vectors to neural embeddings that incorporate language understanding, the system achieves natural language capability while managing complexity through the standardized embedding representation.
Data Source
AI summary
Described herein are machine learning (ML) based on systems, methods, and instrumentalities associated with image search and/or retrieval. An apparatus as described herein may obtain a query image and a textual description associated with the query image, and generate, using an artificial neural network (ANN), a feature representation that may represent the image and the textual description as an associated pair. Based on the feature representation, the apparatus may identify one or more images from an image repository and provide an indication regarding the one or more identified images, for example, as a ranked list.