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
Latest papers with no code
Exploring Content Based Image Retrieval for Highly Imbalanced Melanoma Data using Style Transfer, Semantic Image Segmentation and Ensemble Learning
Lesion images are frequently taken in open-set settings.
An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences
In this paper, a new mobile application for image content retrieval and classification for mobile device display is proposed to enrich the visual experience of users.
Web image search engine based on LSH index and CNN Resnet50
To implement a good Content Based Image Retrieval (CBIR) system, it is essential to adopt efficient search methods.
Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI
Compared with the baseline condition, our PSS reduced the variability in the distance from Alzheimer's disease (AD) to clinically normal (CN) and Parkinson disease (PD) cases by 15. 8-22. 6% and 18. 0-29. 9%, respectively.
Bridging Gap between Image Pixels and Semantics via Supervision: A Survey
The fact that there exists a gap between low-level features and semantic meanings of images, called the semantic gap, is known for decades.
Deep Learning Based Image Retrieval in the JPEG Compressed Domain
Here, we propose a unified model for image retrieval which takes DCT coefficients as input and efficiently extracts global and local features directly in the JPEG compressed domain for accurate image retrieval.
Learning Regional Attention over Multi-resolution Deep Convolutional Features for Trademark Retrieval
However, R-MAC suffers in the presence of background clutter/trivial regions and scale variance, and discards important spatial information.
Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval
To support comparative diagnostic reading, content-based image retrieval (CBIR), which can selectively utilize normal and abnormal features in medical images as two separable semantic components, will be useful.
PrivateMail: Supervised Manifold Learning of Deep Features With Differential Privacy for Image Retrieval
1) We present a novel differentially private method \textit{PrivateMail} for supervised manifold learning, the first of its kind to our knowledge.
An Efficient Framework for Zero-Shot Sketch-Based Image Retrieval
Recently, Zero-shot Sketch-based Image Retrieval (ZS-SBIR) has attracted the attention of the computer vision community due to it's real-world applications, and the more realistic and challenging setting than found in SBIR.
