# CBIR using features derived by Deep Learning

arXiv: https://arxiv.org/abs/2002.07877 · HTML: https://mlsanity.com/paper/2002.07877/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects](https://mlsanity.com/paper/2210.02074/) (2022)
- [Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images](https://mlsanity.com/paper/2405.04211/) (2024)
- [A multimodal deep learning framework for scalable content based visual media retrieval](https://mlsanity.com/paper/2105.08665/) (2021)
- [Group Testing for Accurate and Efficient Range-Based Near Neighbor Search for Plagiarism Detection](https://mlsanity.com/paper/2311.02573/) (2023)
- [Exploring text-to-image generation for historical document image retrieval](https://mlsanity.com/paper/2507.20934/) (2025)

## Abstract (arXiv)

In a Content Based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image. The usual procedure is to extract some useful features from the query image, and retrieve images which have similar set of features. For this purpose, a suitable similarity measure is chosen, and images with high similarity scores are retrieved. Naturally the choice of these features play a very important role in the success of this system, and high level features are required to reduce the semantic gap. In this paper, we propose to use features derived from pre-trained network models from a deep-learning convolution network trained for a large image classification problem. This approach appears to produce vastly superior results for a variety of databases, and it outperforms many contemporary CBIR systems. We analyse the retrieval time of the method, and also propose a pre-clustering of the database based on the above-mentioned features which yields comparable results in a much shorter time in most of the cases.