Browse SoTA > Computer Vision > Image Retrieval > Content-Based Image Retrieval

Content-Based Image Retrieval

15 papers with code · Computer Vision
Subtask of 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

Benchmarks

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Latest papers with code

Privacy Leakage of SIFT Features via Deep Generative Model based Image Reconstruction

2 Sep 2020HighwayWu/SIFT-Reconstruction

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.

CONTENT-BASED IMAGE RETRIEVAL IMAGE RECONSTRUCTION OBJECT RECOGNITION

1
02 Sep 2020

PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks

2 May 2020PyRetri/PyRetri

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.

CONTENT-BASED IMAGE RETRIEVAL

648
02 May 2020

Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images

9 Aug 2019cvjena/eu-flood-dataset

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.

CONTENT-BASED IMAGE RETRIEVAL

16
09 Aug 2019

Content-based image retrieval system with most relevant features among wavelet and color features

6 Feb 2019atina74/atena

In this paper, a new feature extraction schema including the norm of low frequency components in wavelet transformation and color features in RGB and HSV domains are proposed as representative feature vector for images in database followed by appropriate similarity measure for each feature type.

CONTENT-BASED IMAGE RETRIEVAL FEATURE SELECTION

0
06 Feb 2019

Who's Afraid of Adversarial Queries? The Impact of Image Modifications on Content-based Image Retrieval

29 Jan 2019liuzrcc/PIRE

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.

CONTENT-BASED IMAGE RETRIEVAL

13
29 Jan 2019

Classification is a Strong Baseline for Deep Metric Learning

30 Nov 2018azgo14/classification_metric_learning

Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images.

CONTENT-BASED IMAGE RETRIEVAL FACE VERIFICATION METRIC LEARNING

61
30 Nov 2018

Information-Theoretic Active Learning for Content-Based Image Retrieval

7 Sep 2018cvjena/ITAL

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.

ACTIVE LEARNING CONTENT-BASED IMAGE RETRIEVAL

9
07 Sep 2018

Image Super-resolution via Feature-augmented Random Forest

14 Dec 2017HarleyHK/FARF

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.

CONTENT-BASED IMAGE RETRIEVAL DICTIONARY LEARNING DIMENSIONALITY REDUCTION IMAGE SUPER-RESOLUTION SUPER RESOLUTION

2
14 Dec 2017

Automatic Query Image Disambiguation for Content-Based Image Retrieval

2 Nov 2017cvjena/aid

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.

CONTENT-BASED IMAGE RETRIEVAL

12
02 Nov 2017