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 91–100 of 195 papers

TitleStatusHype
A Comparison of CNN and Classic Features for Image Retrieval—0
Genetic Algorithms for the Optimization of Diffusion Parameters in Content-Based Image Retrieval—0
Knowledge Aware Semantic Concept Expansion for Image-Text Matching—0
Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media ImagesCode0
A Fast Content-Based Image Retrieval Method Using Deep Visual Features—0
Deep Learning Approaches for Image Retrieval and Pattern Spotting in Ancient Documents—0
Linking Art through Human Poses—0
Benchmarking unsupervised near-duplicate image detection—0
A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission—0
Efficient Object Embedding for Spliced Image Retrieval—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified