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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 31–40 of 195 papers

TitleStatusHype
Cross-Modality Sub-Image Retrieval using Contrastive Multimodal Image RepresentationsCode0
Hash Function Learning via CodewordsCode0
Content-based image retrieval tutorialCode0
Classification is a Strong Baseline for Deep Metric LearningCode0
A Revisit on Deep Hashings for Large-scale Content Based Image Retrieval—0
A Privacy-Preserving Image Retrieval Scheme with a Mixture of Plain and EtC Images—0
A Fast Content-Based Image Retrieval Method Using Deep Visual Features—0
A Privacy-Preserving Content-Based Image Retrieval Scheme Allowing Mixed Use Of Encrypted And Plain Images—0
A Novel Framework to Jointly Compress and Index Remote Sensing Images for Efficient Content-Based Retrieval—0
Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified