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

Showing 191195 of 195 papers

TitleStatusHype
A Semantically-Aware Relevance Measure for Content-Based Medical Image Retrieval Evaluation0
A Sub-block Based Image Retrieval Using Modified Integrated Region Matching0
A Triplet-loss Dilated Residual Network for High-Resolution Representation Learning in Image Retrieval0
A unified framework of predicting binary interestingness of images based on discriminant correlation analysis and multiple kernel learning0
Autoencoding the Retrieval Relevance of Medical Images0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94Unverified