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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 151–175 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
Large Scale Deep Convolutional Neural Network Features Search with Lucene—0
Image Retrieval with a Bayesian Model of Relevance Feedback—0
Scalable Image Retrieval by Sparse Product Quantization—0
Visual descriptors for content-based retrieval of remote sensing images—0
Local Convolutional Features With Unsupervised Training for Image Retrieval—0
Image Retrieval Based on LBP Pyramidal Multiresolution using Reversible Watermarking—0
Kernelized Deep Convolutional Neural Network for Describing Complex Images—0
A Comparative Analysis of Retrieval Techniques In Content Based Image Retrieval—0
Hash Function Learning via CodewordsCode0
Autoencoding the Retrieval Relevance of Medical Images—0
Techniques for effective and efficient fire detection from social media images—0
Image Retrieval System Base on EMD Similarity Measure and S-Tree—0
Color Image Retrieval Using Fuzzy Measure Hamming and S-Tree—0
Mapping Visual Features to Semantic Profiles for Retrieval in Medical Imaging—0
RBIR using Interest Regions and Binary Signatures—0
Fast Dictionary Matching for Content-based Image Retrieval—0
Exploring EEG for Object Detection and Retrieval—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified