By Topic

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 8 • Date Aug 1998

Filter Results

Displaying Results 1 - 14 of 14
  • Large-scale parallel data clustering

    Page(s): 871 - 876
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (916 KB)  

    Algorithmic enhancements are described that enable large computational reduction in mean square-error data clustering. These improvements are incorporated into a parallel data-clustering tool, P-CLUSTER, designed to execute on a network of workstations. Experiments involving the unsupervised segmentation of standard texture images were performed. For some data sets, a 96 percent reduction in computation was achieved View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • An algorithm for finding the largest approximately common substructures of two trees

    Page(s): 889 - 895
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (140 KB)  

    Ordered, labeled trees are trees in which each node has a label and the left-to-right order of its children (if it has any) is fixed. Such trees have many applications in vision, pattern recognition, molecular biology and natural language processing. We consider a substructure of an ordered labeled tree T to be a connected subgraph of T. Given two ordered labeled trees T1 and T2 and an integer d, the largest approximately common substructure problem is to find a substructure U1 of T1 and a substructure U2 of T2 such that U1 is within edit distance d of U2 and where there does not exist any other substructure V1 of T1 and V2 of T2 such that V1 and V2 satisfy the distance constraint and the sum of the sizes of V1 and V2 is greater than the sum of the sizes of U1 and U2. We present a dynamic programming algorithm to solve this problem, which runs as fast as the fastest known algorithm for computing the edit distance of two trees when the distance allowed in the common substructures is a constant independent of the input trees. To demonstrate the utility of our algorithm, we discuss its application to discovering motifs in multiple RNA secondary structures (which are ordered labeled trees) View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • The random subspace method for constructing decision forests

    Page(s): 832 - 844
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (456 KB)  

    Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom resolved. A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity. The classifier consists of multiple trees constructed systematically by pseudorandomly selecting subsets of components of the feature vector, that is, trees constructed in randomly chosen subspaces. The subspace method is compared to single-tree classifiers and other forest construction methods by experiments on publicly available datasets, where the method's superiority is demonstrated. We also discuss independence between trees in a forest and relate that to the combined classification accuracy View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Unsupervised texture segmentation in a deterministic annealing framework

    Page(s): 803 - 818
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1684 KB)  

    We present a novel optimization framework for unsupervised texture segmentation that relies on statistical tests as a measure of homogeneity. Texture segmentation is formulated as a data clustering problem based on sparse proximity data. Dissimilarities of pairs of textured regions are computed from a multiscale Gabor filter image representation. We discuss and compare a class of clustering objective functions which is systematically derived from invariance principles. As a general optimization framework, we propose deterministic annealing based on a mean-field approximation. The canonical way to derive clustering algorithms within this framework as well as an efficient implementation of mean-field annealing and the closely related Gibbs sampler are presented. We apply both annealing variants to Brodatz-like microtexture mixtures and real-word images View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Incremental learning with sample queries

    Page(s): 883 - 888
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (116 KB)  

    The classical theory of pattern recognition assumes labeled examples appear according to unknown underlying class conditional probability distributions where the pattern classes are picked randomly in a passive manner according to their a priori probabilities. This paper presents experimental results for an incremental nearest-neighbor learning algorithm which actively selects samples from different pattern classes according to a querying rule as opposed to the a priori probabilities. The amount of improvement of this query-based approach over the passive batch approach depends on the complexity of the Bayes rule View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Identification of man-made regions in unmanned aerial vehicle imagery and videos

    Page(s): 852 - 857
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1500 KB)  

    Details work in our group on the use of low-level features for the identification of man-made regions in unmanned aerial vehicle (UAV) imagery. The feature sets that we have examined include classical statistical features such as the coefficient of variation in a window about a pixel, locally computed fractal dimension, and fractal dimension computed in the presence of wavelet boundaries. We discuss these techniques of feature extraction along with our approach to the classification of the features. Our classification work has focused on the use of a semiparametric probability density estimation technique. In addition, we present classification results for region of interest identification based on a set of test images from an UAV test flight View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Breakpoint detection using covariance propagation

    Page(s): 845 - 851
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (316 KB)  

    Presents a statistical approach for detecting breakpoints from chain encoded digital arcs. An arc point is declared as a breakpoint if the estimated orientations of the two fitted lines of the two arc segments immediately to the right and left of the arc point are significantly statistically different. The major contributions of this research include developing a method for analytically estimating the covariance matrix of the fitted line parameters and proposing a perturbation model to characterize the perturbation associated with each arc point View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Partial classification: the benefit of deferred decision

    Page(s): 769 - 776
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (304 KB)  

    It is shown that partial classification which allows for indecision in certain regions of the data space, can increase a benefit function, defined as the difference between the probabilities of correct and incorrect decisions, joint with the event that a decision is made. This is particularly true for small data samples, which may cause a large deviation of the estimated separation surface from the intersection surface between the corresponding probability density functions. Employing a particular density estimation method, an indecision domain is naturally defined by a single parameter whose optimal size, maximizing the benefit function, is derived from the data. The benefit function is shown to translate into profit in stock trading. Employing medical and economic data, it is shown that partial classification produces, on average, higher benefit values than full classification, assigning each new object to a class, and that the marginal benefit of partial classification reduces as the data size increases View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Representation of 3D surfaces by two-variable Fourier descriptors

    Page(s): 858 - 863
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (240 KB)  

    A method for generating two-variable 3D FDs directly from a striped lighting system is developed. An iterative algorithm is proposed to compute the two-variable 3D FDs for both axisymmetric and nonaxisymmetric objects and a formula for convergence test is derived. Experiments conducted for a set of 3D objects show that the iterative algorithm converges very quickly and the two-variable 3D FD representations are attained accurately View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • n-dimensional moment invariants and conceptual mathematical theory of recognition n-dimensional solids

    Page(s): 819 - 831
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (248 KB)  

    The proof of the generalized fundamental theorem of moment invariants (GFTMI) is presented for n-dimensional pattern recognition. On the basis of GFTMI, the moment invariants of affine transformation and subgroups of affine transformation are constructed. Using these invariants, the conceptual mathematical theory of recognition of geometric figures, solids, and their n-dimensional generalizations is worked out. By means of this theory, it is possible for the first time to analyze scenes consisting not only of polygons and polyhedra, but also scenes consisting of geometric figures and solids with curved contours and surfaces, respectively. In general, it is the author's opinion that this theory is a useful step toward the essential development of robot vision and toward creating machine intelligence-to make machines able to think by means of geometric concepts of different generalities and dimensions, and by associations of these concepts View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Selecting the optimal focus measure for autofocusing and depth-from-focus

    Page(s): 864 - 870
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (212 KB)  

    A method is described for selecting the optimal focus measure with respect to gray-level noise from a given set of focus measures in passive autofocusing and depth-from-focus applications. The method is based on two new metrics that have been defined for estimating the noise-sensitivity of different focus measures. The first metric-the autofocusing uncertainty measure (AUM)-is useful in understanding the relation between gray-level noise and the resulting error in lens position for autofocusing. The second metric-autofocusing root-mean-square error (ARMS error)-is an improved metric closely related to AUM. AUM and ARMS error metrics are based on a theoretical noise sensitivity analysis of focus measures, and they are related by a monotonic expression. The theoretical results are validated by actual and simulation experiments. For a given camera, the optimally accurate focus measure may change from one object to the other depending on their focused images. Therefore, selecting the optimal focus measure from a given set involves computing all focus measures in the set View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Optical font recognition using typographical features

    Page(s): 877 - 882
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (104 KB)  

    A new statistical approach based on global typographical features is proposed to the widely neglected problem of font recognition. It aims at the identification of the typeface, weight, slope and size of the text from an image block without any knowledge of the content of that text. The recognition is based on a multivariate Bayesian classifier and operates on a given set of known fonts. The effectiveness of the adopted approach has been experimented on a set of 280 fonts. Font recognition accuracies of about 97 percent were reached on high-quality images. In addition, rates higher than 99.9 percent were obtained for weight and slope detection. Experiments have also shown the system robustness to document language and text content and its sensitivity to text length View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Hilbert-Schmidt lower bounds for estimators on matrix lie groups for ATR

    Page(s): 790 - 802
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (912 KB)  

    Deformable template representations of observed imagery model the variability of target pose via the actions of the matrix Lie groups on rigid templates. In this paper, we study the construction of minimum mean squared error estimators on the special orthogonal group, SO(n), for pose estimation. Due to the nonflat geometry of SO(n), the standard Bayesian formulation of optimal estimators and their characteristics requires modifications. By utilizing Hilbert-Schmidt metric defined on GL(n), a larger group containing SO(n), a mean squared criterion is defined on SO(n). The Hilbert-Schmidt estimate (HSE) is defined to be a minimum mean squared error estimator, restricted to SO(n). The expected error associated with the HSE is shown to be a lower bound, called the Hilbert-Schmidt bound (HSB), on the error incurred by any other estimator. Analysis and algorithms are presented for evaluating the HSE and the HSB in cases of both ground-based and airborne targets View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Fingerprint image enhancement: algorithm and performance evaluation

    Page(s): 777 - 789
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3956 KB)  

    In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We present a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

Aims & Scope

The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is published monthly. Its editorial board strives to present most important research results in areas within TPAMI's scope.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
David A. Forsyth
University of Illinois