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
City-Scale Visual Place Recognition with Deep Local Features Based on Multi-Scale Ordered VLAD Pooling
In this paper, we present a fully-automated system for place recognition at a city-scale based on content-based image retrieval.
iART: A Search Engine for Art-Historical Images to Support Research in the Humanities
In this paper, we introduce iART: an open Web platform for art-historical research that facilitates the process of comparative vision.
Contextual Similarity Aggregation with Self-attention for Visual Re-ranking
Since our re-ranking model is not directly involved with the visual feature used in the initial retrieval, it is ready to be applied to retrieval result lists obtained from various retrieval algorithms.
GPR1200: A Benchmark for General-Purpose Content-Based Image Retrieval
Even though it has extensively been shown that retrieval specific training of deep neural networks is beneficial for nearest neighbor image search quality, most of these models are trained and tested in the domain of landmarks images.
Cross-Modality Sub-Image Retrieval using Contrastive Multimodal Image Representations
mida-group/crossmodal_imgretrieval
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We propose a new application-independent content-based image retrieval (CBIR) system for reverse (sub-)image search across modalities, which combines deep learning to generate representations (embedding the different modalities in a common space) with classical feature extraction and bag-of-words models for efficient and reliable retrieval.
AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image Matching
CBIR with DNNs is generally solved by minimizing a ranking loss, such as Triplet loss (TL), computed on image representations extracted by a DNN from the original data.
NORPPA: NOvel Ringed seal re-identification by Pelage Pattern Aggregation
We propose a method for Saimaa ringed seal (Pusa hispida saimensis) re-identification.
iQPP: A Benchmark for Image Query Performance Prediction
eduard6421/iqpp
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To date, query performance prediction (QPP) in the context of content-based image retrieval remains a largely unexplored task, especially in the query-by-example scenario, where the query is an image.
Integrating Visual and Semantic Similarity Using Hierarchies for Image Retrieval
vaishwarya96/hierarchy-image-retrieval
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Most of the research in content-based image retrieval (CBIR) focus on developing robust feature representations that can effectively retrieve instances from a database of images that are visually similar to a query.
Exploring Masked Autoencoders for Sensor-Agnostic Image Retrieval in Remote Sensing
jakhac/csmae
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We finally derive a guideline to exploit masked image modeling for uni-modal and cross-modal CBIR problems in RS.
