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 61–70 of 195 papers

TitleStatusHype
Content Based Image Indexing and Retrieval—0
Content-based Image Retrieval and the Semantic Gap in the Deep Learning Era—0
Content Based Image Retrieval from AWiFS Images Repository of IRS Resourcesat-2 Satellite Based on Water Bodies and Burnt Areas—0
Content-Based Image Retrieval for Multi-Class Volumetric Radiology Images: A Benchmark Study—0
A Semantically-Aware Relevance Measure for Content-Based Medical Image Retrieval Evaluation—0
Content Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare—0
Content-based image retrieval speedup—0
Content Based Image Retrieval System using Feature Classification with Modified KNN Algorithm—0
Content-based image retrieval system with most relevant features among wavelet and color features—0
Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified