Content-Based Image Retrieval
31 papers with code • 1 benchmarks • 5 datasets
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
Benchmarks
These leaderboards are used to track progress in Content-Based Image Retrieval
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Oxford5k
PS-Battles
GPR1200
European Flood 2013 Dataset
INRIA Holidays Dataset
Most implemented papers
Automatic Query Image Disambiguation for Content-Based Image Retrieval
Query images presented to content-based image retrieval systems often have various different interpretations, making it difficult to identify the search objective pursued by the user.
Image Super-resolution via Feature-augmented Random Forest
In this paper, we present a novel feature-augmented random forest (FARF) for image super-resolution, where the conventional gradient-based features are augmented with gradient magnitudes and different feature recipes are formulated on different stages in an RF.
Information-Theoretic Active Learning for Content-Based Image Retrieval
We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode active learning method for binary classification, and apply it for acquiring meaningful user feedback in the context of content-based image retrieval.
Who's Afraid of Adversarial Queries? The Impact of Image Modifications on Content-based Image Retrieval
liuzrcc/PIRE
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An adversarial query is an image that has been modified to disrupt content-based image retrieval (CBIR) while appearing nearly untouched to the human eye.
Semantic Hierarchy Preserving Deep Hashing for Large-scale Image Retrieval
mzhang367/hpdh
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Deep hashing models have been proposed as an efficient method for large-scale similarity search.
Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images
The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to a coarse distribution of sensors or sensor failures.
Multimedia Retrieval Through Unsupervised Hypergraph-Based Manifold Ranking
In this paper, a novel manifold ranking algorithm is proposed based on the hypergraphs for unsupervised multimedia retrieval tasks.
CBIR using features derived by Deep Learning
In a Content Based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image.
PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks
PyRetri/PyRetri
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Despite significant progress of applying deep learning methods to the field of content-based image retrieval, there has not been a software library that covers these methods in a unified manner.
Privacy Leakage of SIFT Features via Deep Generative Model based Image Reconstruction
HighwayWu/SIFT-Reconstruction
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It is shown that, if the adversary can only have access to the SIFT descriptors while not their coordinates, then the modest success of reconstructing the latent image can be achieved for highly-structured images (e. g., faces) and would fail in general settings.
