# Patchy Image Structure Classification Using Multi-Orientation Region Transform

arXiv: https://arxiv.org/abs/1912.00622 · HTML: https://mlsanity.com/paper/1912.00622/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 13 of 13)

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- [Leaf Cultivar Identification via Prototype-enhanced Learning](https://mlsanity.com/paper/2305.03351/) (2023)
- [Down-Sampling Inter-Layer Adapter for Parameter and Computation Efficient Ultra-Fine-Grained Image Recognition](https://mlsanity.com/paper/2409.11051/) (2024)
- [PGTRNet: Two-phase Weakly Supervised Object Detection with Pseudo Ground Truth Refinement](https://mlsanity.com/paper/2108.11439/) (2021)
- [Mask-Guided Feature Extraction and Augmentation for Ultra-Fine-Grained Visual Categorization](https://mlsanity.com/paper/2109.07755/) (2021)
- [Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships](https://mlsanity.com/paper/2310.15999/) (2023)
- [Geometry-Guided Self-Supervision for Ultra-Fine-Grained Recognition with Limited Data](https://mlsanity.com/paper/2604.19345/) (2026)

## Abstract (arXiv)

Exterior contour and interior structure are both vital features for classifying objects. However, most of the existing methods consider exterior contour feature and internal structure feature separately, and thus fail to function when classifying patchy image structures that have similar contours and flexible structures. To address above limitations, this paper proposes a novel Multi-Orientation Region Transform (MORT), which can effectively characterize both contour and structure features simultaneously, for patchy image structure classification. MORT is performed over multiple orientation regions at multiple scales to effectively integrate patchy features, and thus enables a better description of the shape in a coarse-to-fine manner. Moreover, the proposed MORT can be extended to combine with the deep convolutional neural network techniques, for further enhancement of classification accuracy. Very encouraging experimental results on the challenging ultra-fine-grained cultivar recognition task, insect wing recognition task, and large variation butterfly recognition task are obtained, which demonstrate the effectiveness and superiority of the proposed MORT over the state-of-the-art methods in classifying patchy image structures. Our code and three patchy image structure datasets are available at: https://github.com/XiaohanYu-GU/MReT2019.