Probabilistic consensus clustering using evidence accumulation

The most standard approach to resolve the inherent ambiguities of the non-rigid structure from motion problem is using low-rank models that approximate deforming shapes by a linear combination of rigid basis. These models are typically global, i.e., each shape basis contributes equally to all points of the surface. While this approach has been shown effective to represent smooth deformations, its performance degrades for surfaces composed of various regions, each following a different deformation rule. Piecewise methods attempt to capture this type of behavior by locally modeling surface patches, although they subsequently require enforcing global constraints to assemble back the patches. In this paper we propose an approach that combines the best of global and local models: it locally considers low-rank models but, by construction, does not need to impose global constraints to guarantee local patch continuity. We achieve this by a simple expectation maximization strategy that besides learning global shape bases, it locally adapts their contribution to each specific surface region. Furthermore, as a side contribution, in order to split the surface into different local patches, we propose a novel physically-based mesh segmentation approach that obeys an energy criterion. The complete framework is evaluated in both synthetic and real datasets, and shows an improved performance to competing methods.

show abstract

“…For further details we refer the reader to [27,32]. Figure 2 shows segmentation results on some synthetic objects of the benchmark TOSCA [13].…”

Section: Energy-preserving Segmentation Algorithmmentioning

confidence: 99%

Global Model with Local Interpretation for Dynamic Shape Reconstruction

Agudo

Moreno-Noguer

2017

2017 IEEE Winter Conference on Applications of Computer Vision (WACV)

View full text Add to dashboard Cite

show abstract

“…components) in the shape that are more stable to deformations than the single baseline segmentations. Formally, our approach can be categorized as an unsupervised learning method; the theory behind it is inspired by recent work on consensus clustering [LRBR*13b,LRBR*13a].…”

Section: Consensus Segmentation Of a Shapementioning

confidence: 99%

“…The distance measure (7) has an inherent quadratic complexity, which prevents its direct application to large shapes. In the context of data clustering, to overcome this scalability issue, [LRBR*13b,LRBR*13a] have proposed to artificially sparsify the weights w ij 's, by randomly setting most of them to zero. By doing so, finding the consensus solution to (6) becomes scalable at the price of optimizing a lower bound of the original objective.…”

Section: Consensus Segmentation Of a Shapementioning

confidence: 99%

“…Remark 5 In the case that the number of base segments is too large, one can still resort to an additional sampling strategy geared towards sparsifying the weights ŵ hl , similarly to what done in [LRBR*13b, LRBR*13a]. In Sect.…”

Section: Consensus Segmentation Of a Shapementioning

confidence: 99%

“…To optimize (10), we have implemented the approach proposed in [LRBR*13b, LRBR*13a], properly adapted to cope also with weight matrices having non‐zero diagonal. In fact, the solution proposed in the aforementioned works assumes for technical reasons a weight matrix with null diagonal.…”

Section: Consensus Segmentation Of a Shapementioning

confidence: 99%

See 2 more Smart Citations

Robust Region Detection via Consensus Segmentation of Deformable Shapes

Rodolí

Bulò

Cremers

2014

Computer Graphics Forum

View full text Add to dashboard Cite

ensemble of segmentations consensus segmentationFigure 1: The proposed framework allows to obtain robust surface segmentations in a principled way. Here, an initial ensemble of 50 segmentations (we show only 5 for visualization purposes) is generated via a clustering process on the Global Point Signature embedding of the shape. Given this ensemble, the corresponding consensus is defined as the unknown segmentation that is as close as possible to all the others. Note that the number of regions in the ensemble and in the final consensus segmentation are not necessarily the same. The detected regions are stable across non-rigid deformations of the shape. AbstractWe consider the problem of stable region detection and segmentation of deformable shapes. We pursue this goal by determining a consensus segmentation from a heterogeneous ensemble of putative segmentations, which are generated by a clustering process on an intrinsic embedding of the shape. The intuition is that the consensus segmentation, which relies on aggregate statistics gathered from the segmentations in the ensemble, can reveal components in the shape that are more stable to deformations than the single baseline segmentations. Compared to the existing approaches, our solution exhibits higher robustness and repeatability throughout a wide spectrum of non-rigid transformations. It is computationally efficient, naturally extendible to point clouds, and remains semantically stable even across different object classes. A quantitative evaluation on standard datasets confirms the potentiality of our method as a valid tool for deformable shape analysis.

show abstract

A novel member enhancement‐based clustering ensemble algorithm

He,

Yang,

Cheng

et al. 2024

Concurrency and Computation

View full text Add to dashboard Cite

SummaryClustering ensemble is a popular approach for identifying data clusters that combines the clustering results from multiple base clustering algorithms to produce more accurate and robust data clusters. However, the performance of clustering ensemble algorithms is highly dependent on the quality of clustering members. To address this problem, this paper proposes a member enhancement‐based clustering ensemble (MECE) algorithm that selects the ensemble members by considering their distribution consistency. MECE has two main components, called heterocluster splitting and homocluster merging. The first component estimates two probability density functions (p.d.f.s) estimated on the sample points of an heterocluster and represents them using a Gaussian distribution and a Gaussian mixture model. If the random numbers generated by these two p.d.f.s have different probability distributions, the heterocluster is then split into smaller clusters. The second component merges the clusters that have high neighborhood densities into a homocluster, where the neighborhood density is measured using a novel evaluation criterion. In addition, a co‐association matrix is presented, which serves as a summary for the ensemble of diverse clusters. A series of experiments were conducted to evaluate the feasibility and effectiveness of the proposed ensemble member generation algorithm. Results show that the proposed MECE algorithm can select high quality ensemble members and as a result yield the better clusterings than six state‐of‐the‐art ensemble clustering algorithms, that is, cluster‐based similarity partitioning algorithm (CSPA), meta‐clustering algorithm (MCLA), hybrid bipartite graph formulation (HBGF), evidence accumulation clustering (EAC), locally weighted evidence accumulation (LWEA), and locally weighted graph partition (LWGP). Specifically, MECE algorithm has the nearly 23% higher average NMI, 27% higher average ARI, 15% higher average FMI, and 10% higher average purity than CSPA, MCLA, HBGF, EAC, LWEA, and LWGA algorithms. The experimental results demonstrate that MECE algorithm is a valid approach to deal with the clustering ensemble problems.

show abstract

Probabilistic consensus clustering using evidence accumulation

Cited by 44 publications

References 17 publications

Global Model with Local Interpretation for Dynamic Shape Reconstruction

Global Model with Local Interpretation for Dynamic Shape Reconstruction

Robust Region Detection via Consensus Segmentation of Deformable Shapes

A novel member enhancement‐based clustering ensemble algorithm

Contact Info

Product

Resources

About