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 111–120 of 195 papers

TitleStatusHype
Descriptive analysis of computational methods for automating mammograms with practical applications—0
Detailed Investigation of Deep Features with Sparse Representation and Dimensionality Reduction in CBIR: A Comparative Study—0
Diagnostic Accuracy of Content Based Dermatoscopic Image Retrieval with Deep Classification Features—0
Differential Geometric Retrieval of Deep Features—0
PrivateMail: Supervised Manifold Learning of Deep Features With Differential Privacy for Image Retrieval—0
Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI—0
Domain-invariant feature learning in brain MR imaging for content-based image retrieval—0
Efficient feature embedding of 3D brain MRI images for content-based image retrieval with deep metric learning—0
EndoFinder: Online Image Retrieval for Explainable Colorectal Polyp Diagnosis—0
Learning Test-time Augmentation for Content-based Image Retrieval—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified