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 81–90 of 195 papers

TitleStatusHype
An Effective Automatic Image Annotation Model Via Attention Model and Data Equilibrium—0
BigEarthNet Dataset with A New Class-Nomenclature for Remote Sensing Image Understanding—0
Efficient feature embedding of 3D brain MRI images for content-based image retrieval with deep metric learning—0
Content-based image retrieval speedup—0
Crawler for Image Acquisition from World Wide Web—0
A unified framework of predicting binary interestingness of images based on discriminant correlation analysis and multiple kernel learning—0
Content-based image retrieval using Mix histogram—0
Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval—0
Challenging deep image descriptors for retrieval in heterogeneous iconographic collections—0
VISIR: Visual and Semantic Image Label Refinement—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified