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
Classification is a Strong Baseline for Deep Metric Learning
microsoft/computervision-recipes
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Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images.
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.
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.
Dual-Path Convolutional Image-Text Embeddings with Instance Loss
layumi/Image-Text-Embedding
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In this paper, we propose a new system to discriminatively embed the image and text to a shared visual-textual space.
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.
Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags
Multiple instance learning (MIL) is a variation of traditional supervised learning problems where data (referred to as bags) are composed of sub-elements (referred to as instances) and only bag labels are available.
Content-based image retrieval tutorial
This paper functions as a tutorial for individuals interested to enter the field of information retrieval but wouldn't know where to begin from.
SIFT Meets CNN: A Decade Survey of Instance Retrieval
ChuuyaZZZ/6787-Final-project
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This survey presents milestones in modern instance retrieval, reviews a broad selection of previous works in different categories, and provides insights on the connection between SIFT and CNN-based methods.
Hash Function Learning via Codewords
In this paper we introduce a novel hash learning framework that has two main distinguishing features, when compared to past approaches.
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image.
