Structured Image Knowledge Extraction for Semantic Search Precision
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Solution Overview
Problem
Conventional image search techniques face challenges in achieving high precision semantic image search due to limitations in image tagging and search, leading to errors and misunderstandings in describing images, which results in users having to navigate through numerous images to find the desired one.
Innovation Solution
A digital medium environment is configured to automatically extract and model structured knowledge from images using machine learning, generating a descriptive summarization that describes relationships between objects and the image, enabling accurate image searches and automatic caption generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image tagging techniques are used, then images can be associated with text tags, but the search precision and accuracy deteriorate due to lack of structured relationships between tags and image content
Solution Approach 1:
The patent segments the image into multiple regions and extracts features from each region separately. This segmentation allows the system to capture detailed local information and establish structured relationships between specific image regions and their corresponding tags, thereby improving search precision without requiring an overly complex overall system architecture.
Solution Approach 2:
The patent introduces a new dimension of structured relationships by creating a multi-level feature hierarchy that connects image regions, features, and tags in a structured manner. This dimensional expansion transforms the flat tagging approach into a hierarchical structure, enabling more precise search while maintaining manageable system complexity through organized data representation.
2Reliability
If conventional unstructured tagging is used, then images can be searched using keywords, but the accuracy of matching text with image content deteriorates due to misunderstandings and different interpretations
Solution Approach 1:
The patent performs preliminary action by automatically generating structured tags and descriptions for images during the indexing phase. This preliminary structuring of image data includes extracting features, identifying objects, and establishing relationships before search occurs, which significantly improves matching accuracy and reduces the time users need to spend searching, as the heavy lifting of interpretation is already done.
Solution Approach 2:
The patent introduces an intermediary layer of structured semantic representations that mediate between raw image content and user search queries. This intermediary structure includes hierarchical tag systems and relationship graphs that translate visual content into organized textual representations, improving matching accuracy by reducing ambiguity and decreasing search time through more efficient query processing.
3Measurement precision
If conventional image search techniques are used, then basic keyword matching can be performed, but high precision semantic image search cannot be achieved due to limitations in tag structure and relationships
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate structured tags, extract image features, and establish relationships without requiring manual annotation or user intervention. This automated structuring process achieves high precision semantic search by having the system serve itself in creating the detailed organizational framework needed for accurate matching, thereby improving precision while maintaining practical automation levels.
Data Source
AI summary
Techniques and systems are described to model and extract knowledge from images. A digital medium environment is configured to learn and use a model to compute a descriptive summarization of an input image automatically and without user intervention. Training data is obtained to train a model using machine learning in order to generate a structured image representation that serves as the descriptive summarization of an input image. The images and associated text are processed to extract structured semantic knowledge from the text, which is then associated with the images. The structured semantic knowledge is processed along with corresponding images to train a model using machine learning such that the model describes a relationship between text features within the structured semantic knowledge. Once the model is learned, the model is usable to process input images to generate a structured image representation of the image.