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 161–170 of 195 papers

TitleStatusHype
Web image search engine based on LSH index and CNN Resnet50—0
Image Retrieval using Histogram Factorization and Contextual Similarity Learning—0
On Validation of Search & Retrieval of Tissue Images in Digital Pathology—0
A Bag of Visual Words Model for Medical Image Retrieval—0
Accurate and Fast Pixel Retrieval with Spatial and Uncertainty Aware Hypergraph Diffusion—0
A Genetic Algorithm Approach for ImageRepresentation Learning through Color Quantization—0
A Comparative Analysis of Retrieval Techniques In Content Based Image Retrieval—0
A Comparison of CNN and Classic Features for Image Retrieval—0
A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission—0
A Decade Survey of Content Based Image Retrieval using Deep Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified