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 11–20 of 195 papers

TitleStatusHype
Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media ImagesCode0
Image Super-resolution via Feature-augmented Random ForestCode0
Gray Level Co-Occurrence Matrices: Generalisation and Some New FeaturesCode0
Hash Function Learning via CodewordsCode0
Convex Formulation of Multiple Instance Learning from Positive and Unlabeled BagsCode0
Cross-Modality Sub-Image Retrieval using Contrastive Multimodal Image RepresentationsCode0
Active Learning via Classifier Impact and Greedy Selection for Interactive Image RetrievalCode0
Content-based image retrieval tutorialCode0
CBIR using features derived by Deep LearningCode0
Automatic Query Image Disambiguation for Content-Based Image RetrievalCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified