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 21–30 of 195 papers

TitleStatusHype
An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences—0
A new approach to descriptors generation for image retrieval by analyzing activations of deep neural network layers—0
A new Local Radon Descriptor for Content-Based Image Search—0
An Improved Relevance Feedback in CBIR—0
Annotation Cost Efficient Active Learning for Content Based Image Retrieval—0
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval—0
A Novel Framework to Jointly Compress and Index Remote Sensing Images for Efficient Content-Based Retrieval—0
A Privacy-Preserving Content-Based Image Retrieval Scheme Allowing Mixed Use Of Encrypted And Plain Images—0
A Privacy-Preserving Image Retrieval Scheme with a Mixture of Plain and EtC Images—0
Efficient Object Embedding for Spliced Image Retrieval—0
Show:102550
← PrevPage 3 of 20Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified