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 121–130 of 195 papers

TitleStatusHype
Content Based Image Retrieval from AWiFS Images Repository of IRS Resourcesat-2 Satellite Based on Water Bodies and Burnt Areas—0
Information-Theoretic Active Learning for Content-Based Image RetrievalCode0
A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval—0
Towards Practical Visual Search Engine within Elasticsearch—0
Classifying magnetic resonance image modalities with convolutional neural networks—0
Fractional Local Neighborhood Intensity Pattern for Image Retrieval using Genetic Algorithm—0
Image Super-resolution via Feature-augmented Random ForestCode0
Triagem virtual de imagens de imuno-histoquímica usando redes neurais artificiais e espectro de padrões—0
Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval—0
A Genetic Algorithm Approach for ImageRepresentation Learning through Color Quantization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified