Image Annotation Using Visual Synsets and Weighted Labels
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Solution Overview
Problem
Current image annotation methods face challenges in scalability and accuracy when dealing with large volumes of images, as they often require manual tagging or complex statistical models that are difficult to scale to web-scale environments, leading to issues with false positives and negatives.
Innovation Solution
The proposed solution involves clustering images into visual synsets based on similarity and associating them with weighted labels, using trained classifiers to score and select relevant image groups, and aggregating labels for efficient and accurate annotation of new images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging is used to annotate images, then annotation accuracy is improved, but scalability deteriorates when dealing with millions of images
Solution Approach 1:
The system enables images to annotate themselves by comparing their visual features against pre-computed image groups and their associated labels. Each image automatically determines its own annotations through similarity matching, eliminating the need for manual tagging while maintaining scalability to web-scale environments with millions of images.
2Measurement precision
If complex statistical models are used for image annotation, then annotation accuracy is improved, but device complexity increases making it difficult to scale
Solution Approach 1:
The system performs preliminary organization by pre-computing image groups and their associated labels before actual annotation queries. This advance preparation stores images grouped by visual similarity with pre-determined labels, so that subsequent annotation tasks only require simple similarity comparisons rather than complex statistical model computations, enabling scalability to web-scale environments.
3Loss of time
If image annotation is performed without pre-clustering, then annotation speed is improved for individual images, but overall productivity deteriorates when processing large collections
Solution Approach 1:
The system performs preliminary organization by pre-computing image groups and their associated labels before actual annotation queries. This advance preparation stores images grouped by visual similarity with pre-determined labels, so that subsequent annotation tasks only require simple similarity comparisons rather than complex statistical model computations, enabling scalability to web-scale environments.
Solution Approach 2:
The system segments the large collection of images into smaller, manageable image groups based on visual similarity. Each image group is associated with a set of labels, allowing the annotation process to work with smaller subsets rather than processing all images individually, thereby improving overall processing efficiency for large collections.
Data Source
AI summary
Methods, systems, and articles of manufacture for annotating of an image are disclosed. These include scoring the image using a plurality of trained classifiers, wherein each of the trained classifiers corresponds to at least one of a plurality of image groups clustered based upon image similarity, and wherein each image group is associated with a set of weighted labels; selecting one or more of the image groups based upon the scoring; aggregating one or more sets of weighted labels associated with the selected one or more image groups; and annotating the image using the aggregated one or more sets of weighted labels.