1160887
An objective approach to cluster validation
Cluster validation is a major issue in cluster analysis. Many existing validity indices do not perform well when clusters overlap or there is significant variation in their covariance structure. The contribution of this paper is twofold. First, we propose a new validity index for fuzzy clustering. Second, we present a new approach for the objective evaluation of validity indices and clustering algorithms. Our validity index makes use of the covariance structure of clusters, while the evaluation approach utilizes a new concept of overlap rate that gives a formal measure of the difficulty of distinguishing between overlapping clusters. We have carried out experimental studies using data sets containing clusters of different shapes and densities and various overlap rates, in order to show how validity indices behave when clusters become less and less separable. Finally, the effectiveness of the new validity index is also demonstrated on a number of real-life data sets.
2622680
A direct approach to graph clustering
26802568
Isotropic processing for gradient estimation
This paper concerns the influence of edge direction on the estimation of edge contrast and orientation. We show that the gradient estimated using radial filters is not affected by edge orientation. For non-radial filters the gradient can be affected by edge orientation. For instance, we find that the estimated edge orientation using a non-radial filter may be biased, even if the signal is noise-free. However, there are non-radial filters for which gradient is unaffected by edge orientation as in the case of radial filters. The properties of these functions are given in this paper. The results are illustrated by the study of the Canny, Deriche, and Shen & Castan detectors. We take into account discretization errors. These results give a clear indication of the effect of the rotation invariance property of an edge detector on its response, thus providing a more precise meaning for this property in edge detection.
33546649
Distinguishing between overlapping components in mixture models
14423228
A New Median Graph Algorithm
Median graph is an important new concept introduced to represent a set of graphs by a representative graph. Computing the median graph is an NP-Complete problem. In this paper, we propose an approximate algorithm for computing the median graph. Our algorithm performs in two steps. It first carries out a node reduction process using a clustering method to extract a subset of most representative node labels. It then searches for the median graph candidates from the reduced subset of node labels according to a deterministic strategy to explore the candidate space. Comparison with the genetic search based algorithm will be reported. This algorithm can be used to build a graph clustering algorithm.
16758055
Clustering in Knowledge Embedded Space
Cluster analysis is a fundamental technique in pattern recognition. It is difficult to cluster data on complex data sets. This paper presents a new algorithm for clustering. There are three key ideas in the algorithm: using mutual neighborhood graphs to discover knowledge and cluster data; using eigenvalues of local covariance matrixes to express knowledge and form a knowledge embedded space; and using a denoising trick in knowledge embedded space to implement clustering. Essentially, it learns a new distance metric by knowledge embedding and makes clustering become easier under this distance metric. The experiment results show that the algorithm can construct a quality neighborhood graph from a complex and noisy data set and well solve clustering problems.
1235699
High resolution tracking of non-rigid 3D motion of densely sampled data using harmonic maps
We present a novel fully automatic method for high resolution, nonrigid dense 3D point tracking. High quality dense point clouds of nonrigid geometry moving at video speeds are acquired using a phase-shifting structured light ranging technique. To use such data for the temporal study of subtle motions such as those seen in facial expressions, an efficient nonrigid 3D motion tracking algorithm is needed to establish inter-frame correspondences. The novelty of this paper is the development of an algorithmic framework for 3D tracking that unifies tracking of intensity and geometric features, using harmonic maps with added feature correspondence constraints. While the previous uses of harmonic maps provided only global alignment, the proposed introduction of interior feature constraints guarantees that nonrigid deformations are accurately tracked as well. The harmonic map between two topological disks is a diffeomorphism with minimal stretching energy and bounded angle distortion. The map is stable, insensitive to resolution changes and is robust to noise. Due to the strong implicit and explicit smoothness constraints imposed by the algorithm and the high-resolution data, the resulting registration/deformation field is smooth, continuous and gives dense one-to-one inter-frame correspondences. Our method is validated through a series of experiments demonstrating its accuracy and efficiency.
10710388
CAPTCHA Challenge Tradeoffs: Familiarity of Strings versus Degradation of Images
It is a well documented fact that, for human readers, familiar text is more legible than unfamiliar text. Current-generation computer vision systems also are able to exploit some kinds of prior knowledge of linguistic context: for example, many OCR systems can use known lexica (word-lists, such as of commonly occurring English words) to disambiguate interpretations. It is interesting that human readers can exploit various degrees of familiarity; for example, strings of characters which, while not found in dictionaries, are similar to spelled words: e.g. "pronounceable" strings, or strings made up of frequently occurring character n-grams. In contrast to this, computer vision technologies for exploiting such poorly characterized constraints (absent an explicit, complete lexicon) are not yet well developed. This gap in ability may allow us to design stronger CAPTCHAs. We measure the familiarity of challenge strings generated by four methods (described by Bentley and Mallows) and we use the ScatterType CAPTCHA to degrade challenge images. We report the results of a human legibility trial which supports the hypothesis that more familiar strings are indeed more legible in CAPTCHAs. Our measurements may enable engineering CAPTCHAs with a more uniform distribution of difficulty by balancing image degradations against familiarity
15732177
Network Boosting for BCI Applications
Network Boosting is an ensemble learning method which combines learners together based on a network and can learn the target hypothesis asymptotically. We apply the approach to analyze data from the P300 speller paradigm. The result on the Data set II of BCI (Brain-computer interface) competition III shows that Network Boosting achieves higher classification accuracy than logistic regression, SVM, Bagging and AdaBoost.
5741503
A new scheme on privacy-preserving data classification
We address privacy-preserving classification problem in a distributed system. Randomization has been the approach proposed to preserve privacy in such scenario. However, this approach is now proven to be insecure as it has been discovered that some privacy intrusion techniques can be used to reconstruct private information from the randomized data tuples. We introduce an algebraic-technique-based scheme. Compared to the randomization approach, our new scheme can build classifiers more accurately but disclose less private information. Furthermore, our new scheme can be readily integrated as a middleware with existing systems.