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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 195 papers

TitleStatusHype
Class Agnostic Instance-level Descriptor for Visual Instance Search—0
A Semantically-Aware Relevance Measure for Content-Based Medical Image Retrieval Evaluation—0
OBD-Finder: Explainable Coarse-to-Fine Text-Centric Oracle Bone Duplicates DiscoveryCode0
REJEPA: A Novel Joint-Embedding Predictive Architecture for Efficient Remote Sensing Image Retrieval—0
iCBIR-Sli: Interpretable Content-Based Image Retrieval with 2D Slice Embeddings—0
Domain-invariant feature learning in brain MR imaging for content-based image retrieval—0
Active Learning via Classifier Impact and Greedy Selection for Interactive Image RetrievalCode0
Saliency Map-based Image Retrieval using Invariant Krawtchouk MomentsCode0
Content-Based Image Retrieval Using COSFIRE Descriptors with application to Radio Astronomy—0
CBIDR: A novel method for information retrieval combining image and data by means of TOPSIS applied to medical diagnosis—0
Show:102550
← PrevPage 1 of 20Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified