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Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation
Jordi Pont-Tuset, Pablo Arbelaez, Jonathan T. Barron, Ferran Marques, Jitendra Malik
TL;DR
Object proposal generation depends on accurate proposals, yet existing approaches typically reason over fixed regions and contours from external bottom-up segmenters. MCG unifies multiscale hierarchical segmentation and proposal generation, using coarse-to-fine processing and efficient normalized cuts; across multiple datasets, it achieves state-of-the-art results for contours, hierarchical regions, and proposals.
Problem
Object proposal methods typically learn generic object properties while reasoning over fixed regions and contours produced by external bottom-up segmenters.
Method
MCG unifies multiscale hierarchical segmentation and object proposal generation, exploiting coarse-to-fine processing to accelerate spectral partitioning and create aligned segmentation hierarchies.
Results
MCG achieves state-of-the-art accuracy for contours, hierarchical segmentation, and object proposals across BSDS500, VOC2012, SBD, and COCO evaluations.
Takeaways & Limitations
MCG provides efficient, generalizable ranked proposals that are competitive across proposal-count regimes and have been used in high-performing recognition systems.
Takeaways & Limitations
BSDS500 has a limited range of object scales, yielding modest absolute MCG gains over SCG for boundary evaluation.
Abstract
from arXiv · showhide
We propose a unified approach for bottom-up hierarchical image segmentation and object proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object proposals by exploring efficiently their combinatorial space. We also present Single-scale Combinatorial Grouping (SCG), a faster version of MCG that produces competitive proposals in under five second per image. We conduct an extensive and comprehensive empirical validation on the BSDS500, SegVOC12, SBD, and COCO datasets, showing that MCG produces state-of-the-art contours, hierarchical regions, and object proposals.
1 INTRODUCTION
MCG unifies multiscale hierarchical segmentation and object proposal generation by producing, aligning, and grouping regions across image resolutions. It reports state-of-the-art results for contours, hierarchical regions, and proposals, while SCG offers a faster alternative.
- Object proposal methods often reason over fixed regions and contours from external bottom-up segmenters, limiting how proposals are generated.
- MCG unifies multiscale hierarchical segmentation and object proposal generation in one bottom-up approach.
- MCG contributes an efficient normalized-cuts algorithm, a multiscale hierarchical segmenter, and a grouping algorithm for accurate proposals.
- MCG combines multiscale regions into object proposals by efficiently exploring their combinatorial space.
- MCG obtains overall state-of-the-art accuracy as segmented proposals and bounding boxes on VOC2012, SBD, and COCO datasets.
2 RELATED WORK
Related work spans normalized-cuts acceleration, window and segmented object proposals, learned region merging, and combinatorial grouping. MCG differs by producing and grouping multiscale regions rather than relying on precomputed hierarchies or superpixels.
- Normalized-cuts algorithms target the computational bottleneck of eigenvector computation, often trading accuracy or using reduced representations for speed.
- Object proposal methods generate either image windows or segmented proposals, with MCG focusing on pixel-accurate object extraction.
- Segmented-proposal approaches include seed-based constrained min-cuts, iteratively generated region hierarchies, and selective search over superpixel hierarchies.
- Learned grouping methods train classifiers to decide which regions to merge, specializing decisions by scale, size, or object class.
- Earlier combinatorial grouping combined adjacent regions from multiple segmentations or hierarchical scales to form object proposals.
- MCG produces and groups high-quality multiscale regions, exploring a larger combinatorial space and supporting trade-offs between proposal number and accuracy.
3 THE SEGMENTATION ALGORITHM
The segmentation algorithm combines multiscale processing with hierarchical representations, using fast normalized cuts and alignment to produce coherent multiscale segmentations.
- A segmentation hierarchy is a nested family of partitions, from finest superpixels to the complete image domain.
- A hierarchy can be represented dually as a region dendrogram or an ultrametric contour map, whose thresholded levels produce partitions.
- Fast normalized cuts: 20× speed-up is achieved for normalized-cuts eigenvector computation while preserving full contour-detection performance and reducing memory requirements.
- Fast normalized cuts: DNCuts approximates eigenvectors by recursively squaring and decimating affinity matrices, then upsampling and whitening the results.
- Aligning segmentation hierarchies: Multiscale hierarchies are aligned by projecting each hierarchy level onto a common target segmentation, yielding a hierarchy by construction.
- Aligning segmentation hierarchies: The alignment procedure repeatedly resizes and projects sampled hierarchy levels, with complexity dominated by nearest-neighbor rescaling and projection.
4 EXPERIMENTS ON THE BSDS500
Experiments on BSDS500 show that the multiscale segmenter consistently improves hierarchical segmentation and achieves the best reported boundary and region quality across operating regimes. Its gains over single-scale segmentation are modest for boundaries because BSDS500 contains a limited range of object scales, but larger for objects and parts.
- The evaluation uses BSDS500 with standard metrics and an additional Fop precision-recall metric for objects and parts.
- Single-scale Segmentation: A 20× average speed-up over is obtained by the fast normalized cuts algorithm without sacrificing performance.
- Multiscale Segmentation: Multiscale segmentation consistently improves performance across all metrics and input contour types, with scales N = {0.5, 1, 2} found adequate.
- Multiscale Segmentation: Interpolation artifacts, detail smoothing, and misalignment prevent degraded multiscale variants from improving over the single-scale result.
- Comparison with state-of-the-art: MCG and SCG obtain the best BSDS500 results to date across operating regimes for both boundary and region quality.
- Comparison with state-of-the-art: MCG’s gains over SCG are modest for boundary evaluation but more significant for objects and parts because BSDS500 covers a limited range of object scales.
5 OBJECT PROPOSAL GENERATION
The proposal-generation method treats object segmentation as combinatorial selection of regions from multiscale hierarchies, using efficient tree computations and Pareto-front learning to manage the search. It combines diverse ranked proposal lists and regresses proposal quality to produce likely complete-object hypotheses efficiently.
- Low-level segmentation regions may not represent complete heterogeneous objects, motivating overlapping proposals that better cover full instances.
- Combinatorial Grouping of Proposals: MCG combinatorially selects and merges regions from segmentation hierarchies to generate proposals likely to represent complete objects.
- Fast computation of descriptors: The region-tree approach requires intricate coding and specialized data structures, particularly for computing region neighbors.
- Fast computation of descriptors: Region-tree descriptors are computed by scanning leaf regions once and propagating values upward, reducing area computation from p·(m+1) to p+m.
- Combinatorial Grouping of Proposals: Objects may require n-tuples of regions because hierarchies lack semantic information; for example, one car is optimally represented by three regions.
- Combinatorial Grouping of Proposals: MCG explores region n-tuples from the tree top-down, stopping at thresholds and using efficient neighbor computation instead of exhaustive enumeration.
- Proposal ranking and diversity: Diversity across hierarchies improves proposal results, so MCG combines proposals from multiple scales into ranked lists.
- Parameter Learning via Pareto Front Optimization: Pareto-front optimization balances proposal count against achievable quality when selecting how many proposals to draw from each ranked list.
6 EXPERIMENTS ON PASCAL VOC, SBD, AND COCO
The experiments evaluate MCG's proposal quality, learning efficiency, and generalization across SegVOC12, SBD, and COCO. MCG combines multiscale and combinatorial proposals effectively, while SCG offers competitive results with substantially higher speed.
- Datasets and Evaluation Measures: MCG evaluates achievable proposal quality against proposal count using Jaccard-based measures, including instance-level best overlap and recall at J = 0.5, 0.7, and 0.85.Ji is the mean best overlap across ground-truth instances, while thresholded recall captures the distribution of best overlaps.
- Learning Strategy Evaluation: The greedy learning strategy loses only Ji = 0.0002 on average and Ji = 0.004 (0.74%) at most, while reducing computation from four days to about 20 seconds.The comparison uses a reduced singleton-only full combination, whereas the proposed strategy operates on the full 16 ranked lists.
- Combinatorial Grouping: Adding proposals across three scales significantly improves diversity over raw MCG-UCM-Our, while 2- and 3-region combinations improve achievable quality and 4-tuples contribute only marginally.At the selected working point, Pareto optimization gains 2 points over equal distribution across scales.
- Pareto Selection and Ranking: MCG reduces millions of possible proposals to 5 086 per image while retaining Ji = 0.81 on the SegVOC12 validation set.Regressed ranking reduces the proposal count further below this point.
- Results Across Databases: MCG significantly outperforms the state of the art across proposal-count regimes, with stronger relative results on larger databases and particularly high recall at higher Jaccard thresholds.The results use parameters learned on SegVOC12 for SBD and COCO, and MCG also outperforms for precise bounding-box localization.
- Results Across Databases: SCG achieves very competitive results while being 7× faster than MCG, and MCG and SCG generalize to unseen SBD and COCO images without retraining.The reported generalization uses parameters learned on SegVOC12.
7 CONCLUSIONS
MCG unifies bottom-up segmentation and object proposal generation through multiscale grouping. The paper reports state-of-the-art contours, hierarchical regions, and object proposals, with SCG providing a faster competitive alternative and resources released publicly.
- Conclusions: MCG unifies bottom-up hierarchical segmentation and object proposal generation in a single multiscale approach.The approach is designed for both segmentation and recognition-oriented object proposals.
- Conclusions: A fast normalized-cut eigenvector computation and efficient combinatorial merging of hierarchical regions form MCG's core components.These components support the segmentation and proposal-generation stages.
- Conclusions: MCG produces state-of-the-art contours, hierarchical regions, and object proposals across the paper's evaluated tasks.The validation covers BSDS500, SegVOC12, SBD, and COCO.
- Conclusions: SCG is a sped-up version of the technique that produces competitive results in under five seconds per image.The paper presents SCG as a faster alternative to MCG.
- Conclusions: The project releases code, pre-computed results, and evaluation protocols publicly to promote reproducible research on perceptual grouping.The released resources cover the project's implementation and evaluation materials.