Content-Based Image Retrieval

29 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

Most implemented papers

Content-based image retrieval tutorial

kirk86/ImageRetrieval 12 Aug 2016

This paper functions as a tutorial for individuals interested to enter the field of information retrieval but wouldn't know where to begin from.

Dual-Path Convolutional Image-Text Embeddings with Instance Loss

layumi/Image-Text-Embedding 15 Nov 2017

In this paper, we propose a new system to discriminatively embed the image and text to a shared visual-textual space.

Classification is a Strong Baseline for Deep Metric Learning

azgo14/classification_metric_learning 30 Nov 2018

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

Conditioned and Composed Image Retrieval Combining and Partially Fine-Tuning CLIP-Based Features

abaldrati/clip4cirdemo CVPRW 2022

The proposed method is based on an initial training stage where a simple combination of visual and textual features is used, to fine-tune the CLIP text encoder.

A clinically motivated self-supervised approach for content-based image retrieval of CT liver images

wickstrom/clinical-self-supervised-cbir-ct-liver 11 Jul 2022

We address these limitations by (1) proposing a self-supervised learning framework that incorporates domain-knowledge into the training procedure and (2) providing the first representation learning explainability analysis in the context of CBIR of CT liver images.

Gray Level Co-Occurrence Matrices: Generalisation and Some New Features

adipai/haralick-textural-feature-analysis 22 May 2012

Gray Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis.

Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval

li-xirong/jingwei 28 Mar 2015

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.

Hash Function Learning via Codewords

yinjiehuang/StarSHL 13 Aug 2015

In this paper we introduce a novel hash learning framework that has two main distinguishing features, when compared to past approaches.

SIFT Meets CNN: A Decade Survey of Instance Retrieval

ChuuyaZZZ/6787-Final-project 5 Aug 2016

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.

Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags

levelfour/pumil 22 Apr 2017

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.