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 71–80 of 195 papers

TitleStatusHype
Neuromorphic Computing for Content-based Image Retrieval—0
A new Local Radon Descriptor for Content-Based Image Search—0
A Bag of Visual Words Model for Medical Image Retrieval—0
A new approach to descriptors generation for image retrieval by analyzing activations of deep neural network layers—0
Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation—0
An Improved Relevance Feedback in CBIR—0
Rotation Invariant Deep CBIR—0
Semi-supervised lung nodule retrieval—0
CBIR using features derived by Deep LearningCode0
Learning Test-time Augmentation for Content-based Image Retrieval—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified