Automatic Image Annotation Using Nearest Neighbor Label Transfer
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Solution Overview
Problem
Current image search solutions fail to effectively utilize image content for annotation, leading to limited search results and a lack of relevance in image retrieval, as they rely solely on text-based search, ignoring the visual content of images.
Innovation Solution
A method for automatic image annotation that involves identifying nearest neighbor images using low-level features and combining basic distance measures to assign relevant keywords, employing a greedy label transfer mechanism with minimal training requirements, and utilizing composite distances calculated from global and local image features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If text-based search is used to retrieve images, then search engine simplicity is maintained, but image content effectiveness is lost leading to limited search results
Solution Approach 1:
The patent introduces an intermediary image annotation system that bridges text-based search and image content. The system automatically generates text annotations from image content using nearest neighbor matching, which then serves as the search index. This intermediary layer maintains search engine simplicity while enabling content-based retrieval accuracy.
Solution Approach 2:
The patent applies preliminary action by pre-annotating images with relevant text keywords before search operations. The annotation process uses nearest neighbor image matching to automatically generate descriptive text labels, which are then stored and used for efficient text-based retrieval without requiring complex image analysis during search.
2Reliability
If automatic image annotation is implemented to improve search results, then image content utilization is enhanced, but system complexity increases
Solution Approach 1:
The patent uses copying by replicating label information from nearest neighbor images to the target image. Instead of developing complex annotation algorithms, the system copies proven labels from similar images in the database, achieving accurate annotation through simple label transfer from matched neighbors.
Solution Approach 2:
The annotation system performs self-service by automatically generating annotations without human intervention. The nearest neighbor matching algorithm autonomously identifies similar images and transfers their labels, creating a self-sustaining annotation pipeline that reduces operational complexity.
3Measurement precision
If nearest neighbor matching is used for annotation, then annotation accuracy is improved, but computational requirements increase for large datasets
Solution Approach 1:
The patent applies partial action by computing nearest neighbor distances for only the most relevant features rather than exhaustive image comparison. The system calculates distances based on key visual features to identify sufficient matches without performing complete image analysis, reducing computational overhead while maintaining annotation accuracy.
Data Source
AI summary
Methods, systems, and apparatus, including computer program products, for generating data for annotating images automatically. In one aspect, a method includes receiving an input image, identifying one or more nearest neighbor images of the input image from among a collection of images, in which each of the one or more nearest neighbor images is associated with a respective one or more image labels, assigning a plurality of image labels to the input image, in which the plurality of image labels are selected from the image labels associated with the one or more nearest neighbor images, and storing in a data repository the input image having the assigned plurality of image labels. In another aspect, a method includes assigning a single image label to the input image, in which the single image label is selected from labels associated with multiple ranked nearest neighbor images.