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 71–80 of 195 papers

TitleStatusHype
Content-Based Image Retrieval Using Multiresolution Analysis Of Shape-Based Classified Images—0
Content-Based Image Retrieval Using COSFIRE Descriptors with application to Radio Astronomy—0
Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss—0
Content Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare—0
Automatic Feature Weight Determination using Indexing and Pseudo-Relevance Feedback for Multi-feature Content-Based Image Retrieval—0
Crawler for Image Acquisition from World Wide Web—0
An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences—0
Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval—0
Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors—0
A Self-Balanced Min-Cut Algorithm for Image Clustering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified