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 131–140 of 195 papers

TitleStatusHype
A Revisit on Deep Hashings for Large-scale Content Based Image Retrieval—0
Dual-Path Convolutional Image-Text Embeddings with Instance LossCode0
Automatic Query Image Disambiguation for Content-Based Image RetrievalCode0
MultiNews: A Web collection of an Aligned Multimodal and Multilingual Corpus—0
A Self-Balanced Min-Cut Algorithm for Image Clustering—0
Combining Real-Valued and Binary Gabor-Radon Features for Classification and Search in Medical Imaging Archives—0
Local Neighborhood Intensity Pattern: A new texture feature descriptor for image retrieval—0
Learning Non-Metric Visual Similarity for Image Retrieval—0
Recent Advance in Content-based Image Retrieval: A Literature Survey—0
Convex Formulation of Multiple Instance Learning from Positive and Unlabeled BagsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified