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 21–30 of 195 papers

TitleStatusHype
Content-based image retrieval tutorialCode0
Dual-Path Convolutional Image-Text Embeddings with Instance LossCode0
CBIR using features derived by Deep LearningCode0
Hash Function Learning via CodewordsCode0
Image Super-resolution via Feature-augmented Random ForestCode0
Information-Theoretic Active Learning for Content-Based Image RetrievalCode0
Automatic Query Image Disambiguation for Content-Based Image RetrievalCode0
City-Scale Visual Place Recognition with Deep Local Features Based on Multi-Scale Ordered VLAD PoolingCode0
NORPPA: NOvel Ringed seal re-identification by Pelage Pattern AggregationCode0
Saliency Map-based Image Retrieval using Invariant Krawtchouk MomentsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified