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
On Validation of Search & Retrieval of Tissue Images in Digital Pathology—0
EndoFinder: Online Image Retrieval for Explainable Colorectal Polyp Diagnosis—0
Accurate and Fast Pixel Retrieval with Spatial and Uncertainty Aware Hypergraph Diffusion—0
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval—0
Content-Based Image Retrieval for Multi-Class Volumetric Radiology Images: A Benchmark Study—0
Texture image retrieval using a classification and contourlet-based features—0
Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques—0
Lesion Search with Self-supervised Learning—0
Integrating Visual and Semantic Similarity Using Hierarchies for Image RetrievalCode0
Annotation Cost Efficient Active Learning for Content Based Image Retrieval—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified