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 101–110 of 195 papers

TitleStatusHype
Crawler for Image Acquisition from World Wide Web—0
Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval—0
Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval—0
Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval—0
Deep Features for CBIR with Scarce Data using Hebbian Learning—0
Deep Learning for Instance Retrieval: A Survey—0
Deep Learning Approaches for Image Retrieval and Pattern Spotting in Ancient Documents—0
Deep Learning Based Image Retrieval in the JPEG Compressed Domain—0
Deep Supervised Hashing leveraging Quadratic Spherical Mutual Information for Content-based Image Retrieval—0
Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified