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 131140 of 195 papers

TitleStatusHype
Radiological images and machine learning: trends, perspectives, and prospects0
Radon Features and Barcodes for Medical Image Retrieval via SVM0
RBIR using Interest Regions and Binary Signatures0
Recent Advance in Content-based Image Retrieval: A Literature Survey0
REJEPA: A Novel Joint-Embedding Predictive Architecture for Efficient Remote Sensing Image Retrieval0
Collaborative Group: Composed Image Retrieval via Consensus Learning from Noisy Annotations0
Representing pictures with emotions0
Retrieving Similar X-Ray Images from Big Image Data Using Radon Barcodes with Single Projections0
Rotation Invariant Deep CBIR0
Satellite Image Search in AgoraEO0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94Unverified