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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 51–60 of 195 papers

TitleStatusHype
Exploring Content Based Image Retrieval for Highly Imbalanced Melanoma Data using Style Transfer, Semantic Image Segmentation and Ensemble Learning—0
An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences—0
Web image search engine based on LSH index and CNN Resnet50—0
Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI—0
iART: A Search Engine for Art-Historical Images to Support Research in the HumanitiesCode0
Bridging Gap between Image Pixels and Semantics via Supervision: A Survey—0
Deep Learning Based Image Retrieval in the JPEG Compressed Domain—0
Learning Regional Attention over Multi-resolution Deep Convolutional Features for Trademark Retrieval—0
Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval—0
PrivateMail: Supervised Manifold Learning of Deep Features With Differential Privacy for Image Retrieval—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified