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 101–110 of 195 papers

TitleStatusHype
Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval—0
Simultaneous Feature Aggregating and Hashing for Compact Binary Code Learning—0
Radiological images and machine learning: trends, perspectives, and prospects—0
Unsupervised Multi-modal Hashing for Cross-modal retrieval—0
Dynamic Spatial Verification for Large-Scale Object-Level Image Retrieval—0
Learning Hash Function through Codewords—0
Content-based image retrieval system with most relevant features among wavelet and color features—0
Semantic Hierarchy Preserving Deep Hashing for Large-scale Image RetrievalCode0
Medical Image Super-Resolution Using a Generative Adversarial Network—0
Who's Afraid of Adversarial Queries? The Impact of Image Modifications on Content-based Image RetrievalCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified