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 51–60 of 195 papers

TitleStatusHype
Web image search engine based on LSH index and CNN Resnet50—0
Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI—0
iART: A Search Engine for Art-Historical Images to Support Research in the HumanitiesCode0
Bridging Gap between Image Pixels and Semantics via Supervision: A Survey—0
Deep Learning Based Image Retrieval in the JPEG Compressed Domain—0
Learning Regional Attention over Multi-resolution Deep Convolutional Features for Trademark Retrieval—0
Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval—0
PrivateMail: Supervised Manifold Learning of Deep Features With Differential Privacy for Image Retrieval—0
An Efficient Framework for Zero-Shot Sketch-Based Image Retrieval—0
Deep Learning for Instance Retrieval: A Survey—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified