Automatic Image Annotation via Feature Matching and Keyword Intersection
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Solution Overview
Problem
Existing automatic image annotation techniques require human assistance and cannot efficiently scale to handle the exponentially growing number of images needing annotation, as they necessitate manual labeling of ground truth data or other forms of human interaction.
Innovation Solution
A system that automatically annotates images by extracting features, matching them against a library, identifying similar images on the internet, and annotating with intersecting keywords from the surrounding text, without requiring human intervention, using techniques like partitioning into tiles, generating histograms, and applying probability models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual image annotation techniques are used, then annotation accuracy can be maintained, but the process becomes extremely labor-intensive and expensive
Solution Approach 1:
The system performs automatic image annotation without requiring human intervention. The annotation process is entirely self-service, where the system extracts image features, searches for similar images, and generates annotations autonomously based on textual information from the web, eliminating the need for human indexers while maintaining annotation quality
Solution Approach 2:
The patent replaces the mechanical manual annotation process with an automated computational system. Instead of human indexers manually selecting keywords, the system uses computer vision techniques to extract image features, database queries to find similar images, and text processing to generate annotations, substituting mechanical human labor with automated digital processes
2Extent of automation
If existing automatic annotation techniques are used, then human effort is reduced, but they still require manual labeling of ground truth data or human interaction
Solution Approach 1:
The system achieves full automation by performing all annotation tasks independently without human intervention. It automatically extracts image features using computer vision algorithms, searches for similar images in databases, and generates annotations based on textual information from web pages, making the entire process self-service and eliminating the need for ground truth labeling or human feedback
Solution Approach 2:
The patent introduces an intermediary approach by using existing web-based image databases and textual information as mediators between the input image and the annotation output. The system leverages intermediate resources such as similar image databases and web text to bridge the gap between image processing and annotation generation, avoiding the need for direct human involvement while maintaining system feasibility
3Measurement precision
If manual image annotation is used, then annotation quality can be maintained, but it cannot scale to match the exponentially growing number of images
Solution Approach 1:
The system provides scalable automatic annotation that can handle exponentially growing image collections. By performing all annotation operations autonomously through computer vision algorithms, database searching, and text processing, the system can process millions of images without requiring additional human resources, making it highly scalable while maintaining consistent annotation quality
Solution Approach 2:
The patent creates a universal annotation system that can handle diverse image types and scenarios. The system uses general-purpose image feature extraction techniques, broad database searching capabilities, and flexible text processing to annotate various images across different domains, making it adaptable and versatile for large-scale image collections without requiring domain-specific manual annotation expertise
Data Source
AI summary
One embodiment of the present invention provides a system that automatically annotates an image. During operation, the system receives the image. Next, the system extracts image features from the image. The system then identifies other images which have similar image features. The system next obtains text associated with the other images, and identifies intersecting keywords in the obtained text. Finally, the system annotates the image with the intersecting keywords.