# A Decade Survey of Content Based Image Retrieval using Deep Learning

arXiv: https://arxiv.org/abs/2012.00641 · HTML: https://mlsanity.com/paper/2012.00641/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2020.

## Built on by (most cited first, 30 of 72)

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- [Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks](https://mlsanity.com/paper/2310.19909/) (2023)
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- [A Survey on the Robustness of Computer Vision Models against Common Corruptions](https://mlsanity.com/paper/2305.06024/) (2023)
- [3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in Radiology](https://mlsanity.com/paper/2311.13752/) (2023)
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- [Vision Transformer Hashing for Image Retrieval](https://mlsanity.com/paper/2109.12564/) (2021)
- [Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes](https://mlsanity.com/paper/2309.04302/) (2023)
- [Medical Image Retrieval Using Pretrained Embeddings](https://mlsanity.com/paper/2311.13547/) (2023)
- [Motif Mining: Finding and Summarizing Remixed Image Content](https://mlsanity.com/paper/2203.08327/) (2022)
- [Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss](https://mlsanity.com/paper/2211.15371/) (2022)
- [Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces](https://mlsanity.com/paper/2311.14435/) (2023)
- [LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis](https://mlsanity.com/paper/2405.20592/) (2024)
- [Language Guided Local Infiltration for Interactive Image Retrieval](https://mlsanity.com/paper/2304.07747/) (2023)
- [Survey of Automatic Plankton Image Recognition: Challenges, Existing Solutions and Future Perspectives](https://mlsanity.com/paper/2305.11739/) (2023)
- [Leveraging Foundation Models for Content-Based Image Retrieval in Radiology](https://mlsanity.com/paper/2403.06567/) (2024)
- [Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook](https://mlsanity.com/paper/2505.00630/) (2025)
- [AdaNorm: Adaptive Gradient Norm Correction based Optimizer for CNNs](https://mlsanity.com/paper/2210.06364/) (2022)
- [A Survey of Multimodal Composite Editing and Retrieval](https://mlsanity.com/paper/2409.05405/) (2024)
- [Bridging Gap between Image Pixels and Semantics via Supervision: A Survey](https://mlsanity.com/paper/2107.13757/) (2021)
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- [SPLICE -- Streamlining Digital Pathology Image Processing](https://mlsanity.com/paper/2404.17704/) (2024)
- [Content-Based Image Retrieval for Multi-Class Volumetric Radiology Images: A Benchmark Study](https://mlsanity.com/paper/2405.09334/) (2024)
- [RadIR: A Scalable Framework for Multi-Grained Medical Image Retrieval via Radiology Report Mining](https://mlsanity.com/paper/2503.04653/) (2025)
- [Frequency Disentangled Residual Network](https://mlsanity.com/paper/2109.12556/) (2021)
- [Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology](https://mlsanity.com/paper/2304.08498/) (2023)
- [Learning Image Representations for Content Based Image Retrieval of Radiotherapy Treatment Plans](https://mlsanity.com/paper/2206.02912/) (2022)
- [Guided Unsupervised Learning by Subaperture Decomposition for Ocean SAR Image Retrieval](https://mlsanity.com/paper/2209.15034/) (2022)
- [WSD: Wild Selfie Dataset for Face Recognition in Selfie Images](https://mlsanity.com/paper/2302.07245/) (2023)

## Abstract (arXiv)

The content based image retrieval aims to find the similar images from a large scale dataset against a query image. Generally, the similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. In early days, various hand designed feature descriptors have been investigated based on the visual cues such as color, texture, shape, etc. that represent the images. However, the deep learning has emerged as a dominating alternative of hand-designed feature engineering from a decade. It learns the features automatically from the data. This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval. The categorization of existing state-of-the-art methods from different perspectives is also performed for greater understanding of the progress. The taxonomy used in this survey covers different supervision, different networks, different descriptor type and different retrieval type. A performance analysis is also performed using the state-of-the-art methods. The insights are also presented for the benefit of the researchers to observe the progress and to make the best choices. The survey presented in this paper will help in further research progress in image retrieval using deep learning.