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 61–70 of 195 papers

TitleStatusHype
An Efficient Framework for Zero-Shot Sketch-Based Image Retrieval—0
Deep Learning for Instance Retrieval: A Survey—0
Unsupervised Content based Image Retrieval at Different Precision Level by Combining Multiple Features—0
A Decade Survey of Content Based Image Retrieval using Deep Learning—0
Content-based Image Retrieval and the Semantic Gap in the Deep Learning Era—0
Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval—0
A Privacy-Preserving Content-Based Image Retrieval Scheme Allowing Mixed Use Of Encrypted And Plain Images—0
Descriptive analysis of computational methods for automating mammograms with practical applications—0
City-Scale Visual Place Recognition with Deep Local Features Based on Multi-Scale Ordered VLAD PoolingCode0
Privacy Leakage of SIFT Features via Deep Generative Model based Image ReconstructionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified