# Recent Advance in Content-based Image Retrieval: A Literature Survey

arXiv: https://arxiv.org/abs/1706.06064 · HTML: https://mlsanity.com/paper/1706.06064/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2017.

## Built on by (most cited first, 24 of 24)

- [Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks](https://mlsanity.com/paper/2310.19909/) (2023)
- [Deep Learning for Instance Retrieval: A Survey](https://mlsanity.com/paper/2101.11282/) (2021)
- [DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features](https://mlsanity.com/paper/2108.02927/) (2021)
- [Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language Models](https://mlsanity.com/paper/2404.18746/) (2024)
- [IRGen: Generative Modeling for Image Retrieval](https://mlsanity.com/paper/2303.10126/) (2023)
- [Neural ranking models for document retrieval](https://mlsanity.com/paper/2102.11903/) (2021)
- [Telling the What while Pointing to the Where: Multimodal Queries for Image Retrieval](https://mlsanity.com/paper/2102.04980/) (2021)
- [Deep Triplet Hashing Network for Case-based Medical Image Retrieval](https://mlsanity.com/paper/2101.12346/) (2021)
- [Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrieval](https://mlsanity.com/paper/2602.19961/) (2026)
- [Zero-shot Composed Image Retrieval Considering Query-target Relationship Leveraging Masked Image-text Pairs](https://mlsanity.com/paper/2406.18836/) (2024)
- [DALG: Deep Attentive Local and Global Modeling for Image Retrieval](https://mlsanity.com/paper/2207.00287/) (2022)
- [Towards Cross-modal Backward-compatible Representation Learning for Vision-Language Models](https://mlsanity.com/paper/2405.14715/) (2024)
- [Leveraging BEV Representation for 360-degree Visual Place Recognition](https://mlsanity.com/paper/2305.13814/) (2023)
- [Searching for Pneumothorax in X-Ray Images Using Autoencoded Deep Features](https://mlsanity.com/paper/2102.06096/) (2021)
- [Supporting Experts with a Multimodal Machine-Learning-Based Tool for Human Behavior Analysis of Conversational Videos](https://mlsanity.com/paper/2402.11145/) (2024)
- [DORAEMON: A Unified Library for Visual Object Modeling and Representation Learning at Scale](https://mlsanity.com/paper/2511.04394/) (2025)
- [Ghost-dil-NetVLAD: A Lightweight Neural Network for Visual Place Recognition](https://mlsanity.com/paper/2112.11679/) (2021)
- [Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning](https://mlsanity.com/paper/2304.12448/) (2023)
- [Visual Navigation of Digital Libraries: Retrieval and Classification of Images in the National Library of Norway's Digitised Book Collection](https://mlsanity.com/paper/2410.14969/) (2024)
- [PrivateMail: Supervised Manifold Learning of Deep Features With Differential Privacy for Image Retrieval](https://mlsanity.com/paper/2102.10802/) (2021)
- [Pattern retrieval of traffic congestion using graph-based associations of traffic domain-specific features](https://mlsanity.com/paper/2311.17256/) (2023)
- [MARs: Multi-view Attention Regularizations for Patch-based Feature Recognition of Space Terrain](https://mlsanity.com/paper/2410.05182/) (2024)
- [Self-supervised cross-modality learning for uncertainty-aware object detection and recognition in applications which lack pre-labelled training data](https://mlsanity.com/paper/2411.03082/) (2024)
- [Enhancing Visual Re-ranking through Denoising Nearest Neighbor Graph via Continuous CRF](https://mlsanity.com/paper/2412.13875/) (2024)

## Abstract (arXiv)

The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content-based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research.