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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 141–150 of 195 papers

TitleStatusHype
Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors—0
Differential Geometric Retrieval of Deep Features—0
Retrieving Similar X-Ray Images from Big Image Data Using Radon Barcodes with Single Projections—0
Feature Extraction and Soft Computing Methods for Aerospace Structure Defect Classification—0
Texture and Color-based Image Retrieval Using the Local Extrema Features and Riemannian Distance—0
Content Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare—0
Content-Based Image Retrieval Using Multiresolution Analysis Of Shape-Based Classified Images—0
Stacked Autoencoders for Medical Image Search—0
Content-based image retrieval tutorialCode0
SIFT Meets CNN: A Decade Survey of Instance RetrievalCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified