SOTAVerified

Content-Based Image Retrieval

Content-Based Image Retrieval is a well studied problem in computer vision, with retrieval problems generally divided into two groups: category-level retrieval and instance-level retrieval. Given a query image of the Sydney Harbour bridge, for instance, category-level retrieval aims to find any bridge in a given dataset of images, whilst instance-level retrieval must find the Sydney Harbour bridge to be considered a match.

Source: Camera Obscurer: Generative Art for Design Inspiration

Papers

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Showing 71–80 of 195 papers

TitleStatusHype
An Efficient Framework for Zero-Shot Sketch-Based Image Retrieval—0
Benchmarking unsupervised near-duplicate image detection—0
Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval—0
Automatic tagging and retrieval of E-Commerce products based on visual features—0
An Effective Automatic Image Annotation Model Via Attention Model and Data Equilibrium—0
A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission—0
Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval—0
Crawler for Image Acquisition from World Wide Web—0
Automatic Feature Weight Determination using Indexing and Pseudo-Relevance Feedback for Multi-feature Content-Based Image Retrieval—0
An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified