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 151–160 of 195 papers

TitleStatusHype
Aggregating Binary Local Descriptors for Image Retrieval—0
Visual Relationship Detection with Language Priors—0
Automatic tagging and retrieval of E-Commerce products based on visual features—0
Gabor Barcodes for Medical Image Retrieval—0
Binary Codes for Tagging X-Ray Images via Deep De-Noising Autoencoders—0
Optimizing Top Precision Performance Measure of Content-Based Image Retrieval by Learning Similarity Function—0
Radon Features and Barcodes for Medical Image Retrieval via SVM—0
Generating Binary Tags for Fast Medical Image Retrieval Based on Convolutional Nets and Radon Transform—0
Image Retrieval with a Bayesian Model of Relevance Feedback—0
Large Scale Deep Convolutional Neural Network Features Search with Lucene—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified