# Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features

arXiv: https://arxiv.org/abs/1902.09968 · HTML: https://mlsanity.com/paper/1902.09968/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2019.

## Built on by (most cited first, 19 of 19)

- [Localizing Objects with Self-Supervised Transformers and no Labels](https://mlsanity.com/paper/2109.14279/) (2021)
- [Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut](https://mlsanity.com/paper/2202.11539/) (2022)
- [Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization](https://mlsanity.com/paper/2205.07839/) (2022)
- [TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut](https://mlsanity.com/paper/2209.00383/) (2022)
- [Unsupervised Object Localization: Observing the Background to Discover Objects](https://mlsanity.com/paper/2212.07834/) (2022)
- [Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations](https://mlsanity.com/paper/2207.05027/) (2022)
- [DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery](https://mlsanity.com/paper/2303.09813/) (2023)
- [MOVE: Unsupervised Movable Object Segmentation and Detection](https://mlsanity.com/paper/2210.07920/) (2022)
- [Unsupervised Object Localization in the Era of Self-Supervised ViTs: A Survey](https://mlsanity.com/paper/2310.12904/) (2023)
- [Weakly-supervised Contrastive Learning for Unsupervised Object Discovery](https://mlsanity.com/paper/2307.03376/) (2023)
- [LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-training](https://mlsanity.com/paper/2308.11239/) (2023)
- [Optical Flow boosts Unsupervised Localization and Segmentation](https://mlsanity.com/paper/2307.13640/) (2023)
- [Unsupervised Object Discovery: A Comprehensive Survey and Unified Taxonomy](https://mlsanity.com/paper/2411.00868/) (2024)
- [Unsupervised Object Localization with Representer Point Selection](https://mlsanity.com/paper/2309.04172/) (2023)
- [Masked Multi-Query Slot Attention for Unsupervised Object Discovery](https://mlsanity.com/paper/2404.19654/) (2024)
- [PatchNet: Unsupervised Object Discovery based on Patch Embedding](https://mlsanity.com/paper/2106.08599/) (2021)
- [Leveraging Activations for Superpixel Explanations](https://mlsanity.com/paper/2406.04933/) (2024)
- [PEEKABOO: Hiding parts of an image for unsupervised object localization](https://mlsanity.com/paper/2407.17628/) (2024)
- [Ensemble Foreground Management for Unsupervised Object Discovery](https://mlsanity.com/paper/2507.20860/) (2025)

## Abstract (arXiv)

TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques. We observe that those discovered patterns, i.e., co-occurrence highlighted regions, typically hold appearance and spatial consistency. Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful patterns. Extensive experiments on a variety of benchmarks demonstrate that OLM achieves competitive localization performance compared with the state-of-the-art methods. We also evaluate our approach compared with unsupervised saliency detection methods and achieves competitive results on seven benchmark datasets. Moreover, we conduct experiments on fine-grained classification to show that our proposed method can locate the entire object and parts accurately, which can benefit to improving the classification results significantly.