Semantic Image Region Annotation via Cross-Image Correspondence
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Solution Overview
Problem
Conventional image annotation techniques often associate annotations with entire images rather than specific regions, leading to inaccurate or incomplete annotations, and are impractical due to the time and cost of manual annotation, limiting search logic and indexing capabilities in image retrieval systems.
Innovation Solution
The method involves semantically annotating image regions by identifying corresponding regions in similar images and assigning annotations based on a metric of fit, using image features and similarity metrics to improve annotation accuracy and efficiency, allowing for more precise search queries and indexing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to annotate image regions, then annotation accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The patent copies annotations from source images to target images by identifying corresponding image regions between similar images. Instead of manually annotating each image, the system automatically transfers annotations from images where they already exist, significantly reducing time consumption while maintaining annotation accuracy through the copying process.
Solution Approach 2:
The system enables images to annotate themselves by leveraging annotations that already exist in source images. The annotation process becomes self-service where the annotation information propagates automatically through the image collection based on region correspondences, eliminating the need for continuous manual intervention.
2Ease of operation
If annotations are assigned to entire images rather than specific regions, then annotation process is simplified, but search logic and indexing capabilities are limited
Solution Approach 1:
The patent segments the image into multiple image regions and assigns annotations to specific regions rather than the entire image. This segmentation enables more granular control and allows for sophisticated search logic where users can search for images containing specific objects or features in particular regions, greatly enhancing adaptability while maintaining operational simplicity through automated region identification.
Data Source
AI summary
Techniques for semantically annotating images in a plurality of images, each image in the plurality of images comprising at least one image region. The techniques include identifying at least two similar images including a first image and a second image, identifying corresponding image regions in the first image and the second image, and assigning, using at least one processor, annotations to image regions in one or more images in the plurality of images by using a metric of fit indicative of a degree of match between the assigned annotations and the corresponding image regions. The metric of fit may depend on at least one annotation for each image in a subset of the plurality of images and the identified correspondence between image regions in the first image and the second image.