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 101–110 of 195 papers

TitleStatusHype
Image Retrieval And Classification Using Local Feature Vectors—0
Image Retrieval Based on LBP Pyramidal Multiresolution using Reversible Watermarking—0
Image Retrieval System Base on EMD Similarity Measure and S-Tree—0
Content-based image retrieval using Mix histogram—0
Image Retrieval with a Bayesian Model of Relevance Feedback—0
Kernelized Deep Convolutional Neural Network for Describing Complex Images—0
Knowledge Aware Semantic Concept Expansion for Image-Text Matching—0
Large-margin Learning of Compact Binary Image Encodings—0
Large Scale Deep Convolutional Neural Network Features Search with Lucene—0
Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified