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 171–180 of 195 papers

TitleStatusHype
Convex Formulation of Multiple Instance Learning from Positive and Unlabeled BagsCode0
Active Learning via Classifier Impact and Greedy Selection for Interactive Image RetrievalCode0
Cross-Modality Sub-Image Retrieval using Contrastive Multimodal Image RepresentationsCode0
CBIR using features derived by Deep LearningCode0
Image Super-resolution via Feature-augmented Random ForestCode0
Content-based image retrieval tutorialCode0
Information-Theoretic Active Learning for Content-Based Image RetrievalCode0
Integrating Visual and Semantic Similarity Using Hierarchies for Image RetrievalCode0
iQPP: A Benchmark for Image Query Performance PredictionCode0
Semantic Hierarchy Preserving Deep Hashing for Large-scale Image RetrievalCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified