Research map / Image generation / Image restoration
Automatic Image Cropping : A Computational Complexity Study
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed minimum-rectangle search instead of brute-force search | Problem 1 attention-based cropping · Attention maps with m rows and n columns | computational complexity: O(m2n2) -> O(m2n) | Helped |
| Constrain the crop to a fixed aspect ratio and use Algorithm 4 | Attention-based cropping · Attention maps with m rows and n columns | computational complexity: O(m2n) -> O(mn) | Helped |
| Use Algorithm 4 instead of Algorithm 2 | Automatic image cropping · 1000 randomly selected Microsoft COCO images | average running time: 137.8ms -> 4.2ms | Helped |
| Use Algorithm 4 instead of Algorithm 2 | Automatic image cropping · 1000 randomly selected Microsoft COCO images, τ = 0.9 | average running time: 5.7ms -> 0.9ms | Helped |
| Allow up to two non-intersecting crop rectangles instead of one | Problem 3 cropping · Microsoft COCO example image, τ = 0.75 | cropping area: 0.44 -> 0.22 | Helped |
| Fit log(cropping area) as a power function of log(τ) | Automatic τ selection · 1000 randomly selected Microsoft COCO images | Pearson correlation: 0.995 | Helped |
| Vary τ (ablation) | Problem 1 minimum-area cropping · Microsoft COCO images | crop size and position | Mixed |
| Vary the fixed aspect ratio (ablation) | Problem 2 cropping · Microsoft COCO images | visual crop quality | No clear effect |
| Use the attention model that emphasizes visual importance near the image center (ablation) | Problem 3 multiple-rectangle cropping · Microsoft COCO images | visual crop quality | Hurt |
Closest papers by meaning
- Reliable and Efficient Image Cropping: A Grid Anchor Based Approach (CVPR 2019 · similarity 0.87)
- Cropper: Vision-Language Model for Image Cropping through In-Context Learning (CVPR 2025 · similarity 0.86)
- Spatial-Semantic Collaborative Cropping for User Generated Content (AAAI 2024 · similarity 0.85)
- Learning Subject-Aware Cropping by Outpainting Professional Photos (AAAI 2024 · similarity 0.84)
- Venus: Benchmarking and Empowering Multimodal Large Language Models for Aesthetic Guidance and Cropping (CVPR 2026 · similarity 0.80)
- Training-free Detection of AI-generated images via Cropping Robustness (NeurIPS 2025 · similarity 0.80)
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