Source-linked AI summary
Superpixel Sampling Networks
Varun Jampani, Deqing Sun, Ming-Yu Liu, Ming-Hsuan Yang, Jan Kautz
TL;DR
Existing superpixel algorithms are non-differentiable, limiting their integration with end-to-end deep networks. The paper introduces SSN, which combines deep feature extraction with differentiable SLIC to learn task-specific superpixels. SSNs perform favorably against existing methods, run quickly, and improve downstream semantic segmentation when integrated into a deep network.
Problem
Existing superpixel algorithms are non-differentiable, making them difficult to use within otherwise end-to-end trainable deep neural networks.
Method
SSN combines a deep network that generates pixel features with a differentiable SLIC algorithm for end-to-end superpixel learning.
Results
SSNs consistently perform favorably against state-of-the-art superpixel techniques while being faster, and integrating SSN into a semantic segmentation network improves performance.
Takeaways & Limitations
SSN provides task-specific superpixels that can be learned with flexible loss functions and integrated into other deep networks.
Abstract
from arXiv · showhide
Superpixels provide an efficient low/mid-level representation of image data, which greatly reduces the number of image primitives for subsequent vision tasks. Existing superpixel algorithms are not differentiable, making them difficult to integrate into otherwise end-to-end trainable deep neural networks. We develop a new differentiable model for superpixel sampling that leverages deep networks for learning superpixel segmentation. The resulting "Superpixel Sampling Network" (SSN) is end-to-end trainable, which allows learning task-specific superpixels with flexible loss functions and has fast runtime. Extensive experimental analysis indicates that SSNs not only outperform existing superpixel algorithms on traditional segmentation benchmarks, but can also learn superpixels for other tasks. In addition, SSNs can be easily integrated into downstream deep networks resulting in performance improvements.
1 Introduction
Superpixels offer an efficient image representation, but existing algorithms are non-differentiable and difficult to integrate into end-to-end deep networks. SSN combines deep pixel features with differentiable SLIC to learn task-specific superpixels and achieves favorable benchmark performance and runtime.
- Superpixels group pixels into a perceptually meaningful tessellation that reduces image primitives for subsequent vision algorithms.They are widely used in tasks including object detection, semantic segmentation, saliency estimation, and optical flow.
- Existing superpixel algorithms are non-differentiable, introducing non-differentiable modules into otherwise end-to-end trainable networks.Irregular superpixel lattices also make standard convolution inefficient in most deep architectures.
- SSN combines a deep network for pixel features with differentiable SLIC, whose relaxed associations enable end-to-end training and task-specific superpixel learning.The framework uses flexible loss functions to learn superpixels for different vision tasks.
- SSNs are end-to-end trainable, flexible for task-specific learning, and easily integrated into other deep network architectures.The paper presents this as the first end-to-end trainable superpixel algorithm to the authors’ knowledge.
- SSN outperforms existing superpixel algorithms across benchmark datasets while maintaining favorable runtime.Experiments include BSDS500, Cityscapes, and PascalVOC.
2 Related Work
Prior superpixel methods include graph-based, clustering-based, and other specialized approaches, but they generally rely on hand-crafted features. Deep networks have been explored for clustering and, more recently, for learning superpixel features despite non-differentiability.
- Graph-based methods formulate superpixel segmentation as graph partitioning, including normalized cuts, FH, and ERS.Pixels serve as graph nodes and adjacency edges encode connectivity strength.
- Clustering-based methods use techniques such as k-means, with SLIC, LSC, and Manifold-SLIC differing mainly in feature representation.SLIC uses five-dimensional XY Lab features, while LSC projects those features into a higher-dimensional representation.
- Other superpixel techniques include watershed, geometric flows, graph-cuts, mean-shift, and hill-climbing, but these methods rely on hand-crafted features.The paper notes that incorporating deep networks into them is non-trivial.
- Deep clustering research uses deep networks to model cluster assignments or latent variables and supports end-to-end training of iterative procedures.Examples include neural expectation maximization and Ladder-network-based clustering.
3 Preliminaries
SLIC is a widely used k-means superpixel algorithm operating in a five-dimensional position-and-color space. It alternates pixel-to-superpixel assignment with cluster-center updates, typically restricting distance computations to local neighborhoods for efficiency.
- SLIC is widely used because it is simple to implement, fast, and produces compact, uniform superpixels.
- SLIC performs k-means clustering on image pixels represented in a five-dimensional XY Lab position-and-color space.Its goal is to assign each pixel to one of m superpixels.
- SLIC initializes superpixel centers by sampling points across the pixel grid, with local perturbations based on image gradients.The initial centers are represented as S0 in the five-dimensional feature space.
- Each SLIC iteration associates pixels with nearby superpixel centers and updates centers by averaging features within each cluster.The two steps repeat until convergence or for a fixed number of iterations.
- SLIC restricts distance calculations to a fixed neighborhood around each superpixel center to reduce computation.Spatial connectivity can optionally be enforced after clustering.
4 Superpixel Sampling Networks
SSN couples a CNN that generates pixel features with a differentiable version of SLIC, replacing hard nearest-neighbor associations with soft associations and iterative updates. The resulting representation supports differentiable pixel–superpixel mappings and task-specific learning through reconstruction and compactness losses.
- Architecture: SSN combines a deep feature-extraction network with differentiable SLIC to learn superpixels.The CNN features are passed to iterative differentiable SLIC updates.
- Differentiable SLIC: Soft pixel–superpixel associations replace SLIC’s non-differentiable nearest-neighbor operation.The associations are represented by Q ∈ R^n×m and used to update superpixel centers through weighted pixel features.
- Differentiable SLIC: Each pixel is compared with only 9 surrounding superpixels, reducing the association matrix from n × m to n × 9.This locality constraint reduces computation and memory requirements.
- Algorithm: SSN iteratively initializes centers from a regular grid, updates associations and centers, and can optionally produce hard clusters or enforce connectivity.The optional hard-association and connectivity steps are not differentiable.
- Mappings: Soft associations enable differentiable mappings between pixel and superpixel representations using matrix multiplications.These mappings can support downstream networks without requiring non-differentiable hard clusters.
- Learning Task-Specific Superpixels: Task-specific reconstruction and compactness losses train superpixels to represent chosen pixel properties while encouraging spatial compactness.Pixel properties may include semantic labels or optical flow; the overall loss is L = Lrecon + λLcompact with λ = 10^-5 in the experiments.
5 Experiments
Experiments evaluate SSN on superpixel benchmarks, semantic segmentation, optical flow, runtime, and downstream integration. SSNdeep benefits from deep features, performs favorably against established methods, and improves a downstream CNN.
- Experiments cover BSDS500, Cityscapes, PascalVOC, and MPI-Sintel, including benchmark evaluation, task-specific superpixels, runtime, and downstream semantic segmentation.
- 5.1 Learned Superpixels: SSNlinear exceeds SSNpix on ASA and BR, while SSNdeep improves both scores substantially; higher k and v provide smaller gains.The selected SSNdeep configuration uses k = 20 and v = 10.
- 5.1 Learned Superpixels: SSN performs considerably better than prominent superpixel algorithms on BSDS500 in ASA and precision-recall, while SSNdeep follows object boundaries more closely.Comparisons include SLIC, SNIC, SEEDS, LSC, ERS, ETPS, and SCALP.
- 5.2 Superpixels for Semantic Segmentation: On Cityscapes, SSNdeep matches SEAL in ASA and achieves better precision-recall, while runtime analysis finds SSN faster than several implementations.Runtime is measured for 1000 superpixels on a 512 × 1024 image.
- 5.2 Superpixels for Semantic Segmentation: SSN integration into a bilateral inception semantic-segmentation network improves IoU relative to the original SLIC superpixels.
- 5.3 Superpixels for Optical Flow: SSN also learns superpixels for optical flow by using ground-truth flow as pixel properties and evaluating segmented-flow Euclidean error.Visual results indicate SSNdeep better represents ground-truth optical flow than other techniques.
6 Conclusion
The paper presents SSN as an end-to-end trainable, task-specific superpixel method that addresses the non-differentiability of existing algorithms. Across benchmarks, it performs favorably and runs faster, while integration into a semantic-segmentation network improves performance.
- SSN estimates task-specific superpixels using deep features learned through end-to-end training.
- Across several benchmarks, SSN performs favorably against state-of-the-art superpixel techniques and is faster.
- Integrating SSN into a semantic-segmentation network results in performance improvements.
- SSN addresses the non-differentiability of existing superpixel algorithms, supporting their incorporation into deep networks.The paper also notes that superpixels can reduce computational complexity and support piece-wise constant assumptions and long-range information propagation.
A Supplementary Material
The supplementary material formalizes ASA and adds evaluation metrics, visual results, and an overview video for SSN.
- Supplementary Section A.1 formally defines Achievable Segmentation Accuracy, while Section A.2 reports F-measure, Compactness, and additional visual results.
- The supplementary material includes a video overview of Superpixel Sampling Networks and experimental results.
A.1 Evaluation Metrics
The evaluation uses ASA to quantify the segmentation accuracy achievable from superpixels and Boundary Precision-Recall to assess boundary alignment. It also reports F-measure and Compactness using default evaluation scripts.
- ASA: ASA is defined for a superpixel segmentation H with m disjoint segments and a ground-truth segmentation G with w segments.Each segment is represented as H_j or G_l.
- ASA: ASA compares each superpixel segment with its most-overlapping ground-truth segment and normalizes the summed overlap by the number of image pixels.It represents an upper bound on accuracy achievable by a segmentation step performed on the superpixels.
- Boundary Precision-Recall: Boundary Recall measures how well superpixel boundaries align with ground-truth boundaries, but higher recall does not necessarily indicate more useful superpixels.The paper therefore reports Boundary Precision-Recall curves instead of only Boundary Recall scores.
- Additional metrics: F-measure and Compactness are also evaluated using scripts from with default parameters.These metrics are reported in the subsequent experimental section.
A.2 Additional Experimental Results
Additional experiments show that SSNdeep improves boundary-related evaluation while preserving compactness, and produces task-relevant visual structures across several datasets and optical flow.
- Compactness and F-measure: SSNdeep has only slightly lower Compactness than SLIC, while maintaining Compactness similar to SSNpix as ASA and boundary adherence improve.The compactness loss weight can be increased when more compact superpixels are desired.
- Compactness and F-measure: SSNdeep outperforms other techniques in F-measure while maintaining the Compactness of SSNpix.The authors describe this as robustness across different superpixel aspects.
- Visual results: On BSDS500 and PascalVOC, SSNdeep tends to produce smoother object contours and more superpixels near object boundaries.The comparisons include SLIC, LSC, and ERS.
- Visual results: On Cityscapes, SSNdeep tends to generate larger superpixels in uniform regions and more superpixels on smaller objects.Road regions are given as an example of uniform content.
- Visual results: On MPI-Sintel, SSNdeep superpixels can better represent ground-truth optical flow than other techniques.The comparison uses segmented flow visualizations obtained with different superpixel types.