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 151–160 of 195 papers

TitleStatusHype
Linking Art through Human Poses—0
Local Convolutional Features With Unsupervised Training for Image Retrieval—0
Local Neighborhood Intensity Pattern: A new texture feature descriptor for image retrieval—0
Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval—0
Low-Level Features for Image Retrieval Based on Extraction of Directional Binary Patterns and Its Oriented Gradients Histogram—0
Mapping Visual Features to Semantic Profiles for Retrieval in Medical Imaging—0
Multimorbidity Content-Based Medical Image Retrieval Using Proxies—0
MultiNews: A Web collection of an Aligned Multimodal and Multilingual Corpus—0
Dynamic Spatial Verification for Large-Scale Object-Level Image Retrieval—0
Neuromorphic Computing for Content-based Image Retrieval—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified