S-TREE: self-organizing trees for data clustering and online vector quantization

Campos, Marcos M.; Carpenter, Gail A.

doi:10.1016/s0893-6080(01)00020-x

Cited by 46 publications

(26 citation statements)

References 37 publications

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“…The idea is to cache a set of image exemplars, each inducing its own discriminant subspace. Given a new video frame, the algorithm quickly searches through the exemplar database indexed with an on-line version of tree-structured vector quantization, S-tree [129]. If a similar exemplar is found, the face detector will be skipped and the previously detected object states will be reused.…”

Section: Variations Of the Boosting Learning Algorithmmentioning

confidence: 99%

A survey on face detection in the wild: Past, present and future

Zafeiriou

Cha

Zhang

2015

Computer Vision and Image Understanding

386

167

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Section: Variations Of the Boosting Learning Algorithmmentioning

confidence: 99%

A survey on face detection in the wild: Past, present and future

Zafeiriou

Cha

Zhang

2015

Computer Vision and Image Understanding

386

167

View full text Add to dashboard Cite

“…Parameters in the S-TREE algorithm are shown in Table 1, where T shows a window size. These parameters were set according to Campos and Carpenter (8) . Training was performed by changing of the p significance level.…”

Section: Experiments On Vector Quantizationmentioning

confidence: 99%

“…Tree-structured clustering is fast, scale well (in processing time) with the number of feature dimensions and clusters, and can capture hierarchical structures in the data (6) (7) . Campos and Carpenter proposed self-organizing tree (S-TREE), a family of methods that enables hierarchical representation of data (8) . S-TREE models solve clustering problems by imposing tree-structured constraints on the solution.…”

Section: Introductionmentioning

confidence: 99%

Self-Organizing Tree Using Cluster Validity

et al. 2004

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Self-organizing tree (S-TREE) models solve clustering problems by imposing tree-structured constraints on the solution. It has a self-organizing capacity and has better performance than previous tree-structured algorithms. S-TREE carries out pruning to reduce the effect of bad leaf nodes when the tree reaches a predetermined maximum size (U ), However, it is difficult to determine U beforehand because it is problemdependent. U gives the limit of tree growth and can also prevent self-organization of the tree. It may produce an unnatural clustering. In this paper, we propose an algorithm for pruning algorithm that does not require U . This algorithm prunes extra nodes based on a significant level of cluster validity and allows the S-TREE to grow by a self-organization. The performance of the new algorithm was examined by experiments on vector quantization. The results of experiments show that natural leaf nodes are formed by this algorithm without setting the limit for the growth of the S-TREE.

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“…1(a). The small closed circle indicated by the number 1 is a root node of the S-TREE, and the larger closed circle indicated by (1) shows the location of the cluster center. The tree structure after three steps of the algorithm have been carried out is shown in Fig.…”

Section: Pruning Algorithmmentioning

confidence: 99%

“…This is called tree-structured clustering. Campos and Carpenter proposed self-organizing tree (S-TREE) (1) . This tree has a self-organizing capability and has better performance than previously reported tree-structured algorithms.…”

Section: Introductionmentioning

confidence: 99%

Clustering with an Improved Self-Organizing Tree

Suzuki

Sasaki

2004

IEEJ Trans.EIS

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A self-organizing tree (S-TREE) has a self-organizing capability and better performance than previously reported tree-structured clustering. In the S-TREE algorithm, since a tree grows in greedy fashion, a pruning mechanism is necessary to reduce the effect of bad leaf nodes. Extra nodes are pruned when the tree reaches a predetermined maximum size (U ). U is problem-dependent and is therefore difficult to specify beforehand. Furthermore, since U gives the limit of tree growth and also prevents self-organizing of the tree, it may produce unnatural clustering. We are presenting a new pruning algorithm without U . In this paper, we present results showing the performance of the new pruning algorithm using samples generated from normal distributions. The results of computational experiments showed that the new pruning algorithm works well for clustering of those samples.

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S-TREE: self-organizing trees for data clustering and online vector quantization

Cited by 46 publications

References 37 publications

A survey on face detection in the wild: Past, present and future

A survey on face detection in the wild: Past, present and future

Self-Organizing Tree Using Cluster Validity

Clustering with an Improved Self-Organizing Tree

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