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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 12 • Date Dec. 2003

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Displaying Results 1 - 17 of 17
  • Multiresolution estimates of classification complexity

    Page(s): 1534 - 1539
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (793 KB) |  | HTML iconHTML  

    In this paper, we study two measures of classification complexity based on feature space partitioning: purity and neighborhood separability. The new measures of complexity are compared with probabilistic distance measures and a number of other nonparametric estimates of classification complexity on a total of 10 databases from the University of California, Irvine, (UCI) repository. View full abstract»

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  • A general model for finite-sample effects in training and testing of competing classifiers

    Page(s): 1561 - 1569
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (552 KB) |  | HTML iconHTML  

    The conventional wisdom in the field of statistical pattern recognition (SPR) is that the size of the finite test sample dominates the variance in the assessment of the performance of a classical or neural classifier. The present work shows that this result has only narrow applicability. In particular, when competing algorithms are compared, the finite training sample more commonly dominates this uncertainty. This general problem in SPR is analyzed using a formal structure recently developed for multivariate random-effects receiver operating characteristic (ROC) analysis. Monte Carlo trials within the general model are used to explore the detailed statistical structure of several representative problems in the subfield of computer-aided diagnosis in medicine. The scaling laws between variance of accuracy measures and number of training samples and number of test samples are investigated and found to be comparable to those discussed in the classic text of Fukunaga, but important interaction terms have been neglected by previous authors. Finally, the importance of the contribution of finite trainers to the uncertainties argues for some form of bootstrap analysis to sample that uncertainty. The leading contemporary candidate is an extension of the 0.632 bootstrap and associated error analysis, as opposed to the more commonly used cross-validation. View full abstract»

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  • Exploitation of unlabeled sequences in hidden Markov models

    Page(s): 1570 - 1581
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (724 KB) |  | HTML iconHTML  

    This paper presents a method for effectively using unlabeled sequential data in the learning of hidden Markov models (HMMs). With the conventional approach, class labels for unlabeled data are assigned deterministically by HMMs learned from labeled data. Such labeling often becomes unreliable when the number of labeled data is small. We propose an extended Baum-Welch (EBW) algorithm in which the labeling is undertaken probabilistically and iteratively so that the labeled and unlabeled data likelihoods are improved. Unlike the conventional approach, the EBW algorithm guarantees convergence to a local maximum of the likelihood. Experimental results on gesture data and speech data show that when labeled training data are scarce, by using unlabeled data, the EBW algorithm improves the classification performance of HMMs more robustly than the conventional naive labeling (NL) approach. View full abstract»

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  • Estimating piecewise-smooth optical flow with global matching and graduated optimization

    Page(s): 1625 - 1630
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (631 KB) |  | HTML iconHTML  

    This paper presents a new method for estimating piecewise-smooth optical flow. We propose a global optimization formulation with three-frame matching and local variation and develop an efficient technique to minimize the resultant global energy. This technique takes advantage of local gradient, global gradient, and global matching methods and alleviates their limitations. Experiments on various synthetic and real data show that this method achieves highly competitive accuracy. View full abstract»

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  • Correspondence matching with modal clusters

    Page(s): 1609 - 1615
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (968 KB) |  | HTML iconHTML  

    The modal correspondence method of Shapiro and Brady aims to match point-sets by comparing the eigenvectors of a pairwise point proximity matrix. Although elegant by means of its matrix representation, the method is notoriously susceptible to differences in the relational structure of the point-sets under consideration. In this paper, we demonstrate how the method can be rendered robust to structural differences by adopting a hierarchical approach. To do this, we place the modal matching problem in a probabilistic setting in which the correspondences between pairwise clusters can be used to constrain the individual point correspondences. We demonstrate the utility of the method on a number of synthetic and real-world point-pattern matching problems. View full abstract»

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  • Optimal cluster preserving embedding of nonmetric proximity data

    Page(s): 1540 - 1551
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (854 KB) |  | HTML iconHTML  

    For several major applications of data analysis, objects are often not represented as feature vectors in a vector space, but rather by a matrix gathering pairwise proximities. Such pairwise data often violates metricity and, therefore, cannot be naturally embedded in a vector space. Concerning the problem of unsupervised structure detection or clustering, in this paper, a new embedding method for pairwise data into Euclidean vector spaces is introduced. We show that all clustering methods, which are invariant under additive shifts of the pairwise proximities, can be reformulated as grouping problems in Euclidian spaces. The most prominent property of this constant shift embedding framework is the complete preservation of the cluster structure in the embedding space. Restating pairwise clustering problems in vector spaces has several important consequences, such as the statistical description of the clusters by way of cluster prototypes, the generic extension of the grouping procedure to a discriminative prediction rule, and the applicability of standard preprocessing methods like denoising or dimensionality reduction. View full abstract»

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  • Personal identification based on iris texture analysis

    Page(s): 1519 - 1533
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2664 KB) |  | HTML iconHTML  

    With an increasing emphasis on security, automated personal identification based on biometrics has been receiving extensive attention over the past decade. Iris recognition, as an emerging biometric recognition approach, is becoming a very active topic in both research and practical applications. In general, a typical iris recognition system includes iris imaging, iris liveness detection, and recognition. This paper focuses on the last issue and describes a new scheme for iris recognition from an image sequence. We first assess the quality of each image in the input sequence and select a clear iris image from such a sequence for subsequent recognition. A bank of spatial filters, whose kernels are suitable for iris recognition, is then used to capture local characteristics of the iris so as to produce discriminating texture features. Experimental results show that the proposed method has an encouraging performance. In particular, a comparative study of existing methods for iris recognition is conducted on an iris image database including 2,255 sequences from 213 subjects. Conclusions based on such a comparison using a nonparametric statistical method (the bootstrap) provide useful information for further research. View full abstract»

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  • Neural edge enhancer for supervised edge enhancement from noisy images

    Page(s): 1582 - 1596
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2902 KB) |  | HTML iconHTML  

    We propose a new edge enhancer based on a modified multilayer neural network, which is called a neural edge enhancer (NEE), for enhancing the desired edges clearly from noisy images. The NEE is a supervised edge enhancer: Through training with a set of input noisy images and teaching edges, the NEE acquires the function of a desired edge enhancer. The input images are synthesized from noiseless images by addition of noise. The teaching edges are made from the noiseless images by performing the desired edge enhancer. To investigate the performance, we carried out experiments to enhance edges from noisy artificial and natural images. By comparison with conventional edge enhancers, the following was demonstrated: The NEE was robust against noise, was able to enhance continuous edges from noisy images, and was superior to the conventional edge enhancers in similarity to the desired edges. To gain insight into the nonlinear kernel of the NEE, we performed analyses on the trained NEE. The results suggested that the trained NEE acquired directional gradient operators with smoothing. Furthermore, we propose a method for edge localization for the NEE. We compared the NEE, together with the proposed edge localization method, with a leading edge detector. The NEE was proven to be useful for enhancing edges from noisy images. View full abstract»

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  • Face recognition in hyperspectral images

    Page(s): 1552 - 1560
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1354 KB) |  | HTML iconHTML  

    Hyperspectral cameras provide useful discriminants for human face recognition that cannot be obtained by other imaging methods. We examine the utility of using near-infrared hyperspectral images for the recognition of faces over a database of 200 subjects. The hyperspectral images were collected using a CCD camera equipped with a liquid crystal tunable filter to provide 31 bands over the near-infrared (0.7 μm-1.0 μm). Spectral measurements over the near-infrared allow the sensing of subsurface tissue structure which is significantly different from person to person, but relatively stable over time. The local spectral properties of human tissue are nearly invariant to face orientation and expression which allows hyperspectral discriminants to be used for recognition over a large range of poses and expressions. We describe a face recognition algorithm that exploits spectral measurements for multiple facial tissue types. We demonstrate experimentally that this algorithm can be used to recognize faces over time in the presence of changes in facial pose and expression. View full abstract»

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  • Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm

    Page(s): 1631 - 1639
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1248 KB) |  | HTML iconHTML  

    The current paper presents a novel texture-based method for detecting texts in images. A support vector machine (SVM) is used to analyze the textural properties of texts. No external texture feature extraction module is used, but rather the intensities of the raw pixels that make up the textural pattern are fed directly to the SVM, which works well even in high-dimensional spaces. Next, text regions are identified by applying a continuously adaptive mean shift algorithm (CAMSHIFT) to the results of the texture analysis. The combination of CAMSHIFT and SVMs produces both robust and efficient text detection, as time-consuming texture analyses for less relevant pixels are restricted, leaving only a small part of the input image to be texture-analyzed. View full abstract»

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  • Motion recognition using nonparametric image motion models estimated from temporal and multiscale co-occurrence statistics

    Page(s): 1619 - 1624
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (591 KB) |  | HTML iconHTML  

    A new approach for motion characterization in image sequences is presented. It relies on the probabilistic modeling of temporal and scale co-occurrence distributions of local motion-related measurements directly computed over image sequences. Temporal multiscale Gibbs models allow us to handle both spatial and temporal aspects of image motion content within a unified statistical framework. Since this modeling mainly involves the scalar product between co-occurrence values and Gibbs potentials, we can formulate and address several fundamental issues: model estimation according to the ML criterion (hence, model training and learning) and motion classification. We have conducted motion recognition experiments over a large set of real image sequences comprising various motion types such as temporal texture samples, human motion examples, and rigid motion situations. View full abstract»

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  • Vision and inertial sensor cooperation using gravity as a vertical reference

    Page(s): 1597 - 1608
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (973 KB) |  | HTML iconHTML  

    This paper explores the combination of inertial sensor data with vision. Visual and inertial sensing are two sensory modalities that can be explored to give robust solutions on image segmentation and recovery of 3D structure from images, increasing the capabilities of autonomous robots and enlarging the application potential of vision systems. In biological systems, the information provided by the vestibular system is fused at a very early processing stage with vision, playing a key role on the execution of visual movements such as gaze holding and tracking, and the visual cues aid the spatial orientation and body equilibrium. In this paper, we set a framework for using inertial sensor data in vision systems, and describe some results obtained. The unit sphere projection camera model is used, providing a simple model for inertial data integration. Using the vertical reference provided by the inertial sensors, the image horizon line can be determined. Using just one vanishing point and the vertical, we can recover the camera's focal distance and provide an external bearing for the system's navigation frame of reference. Knowing the geometry of a stereo rig and its pose from the inertial sensors, the collineations of level planes can be recovered, providing enough restrictions to segment and reconstruct vertical features and leveled planar patches. View full abstract»

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  • Silhouette analysis-based gait recognition for human identification

    Page(s): 1505 - 1518
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1091 KB) |  | HTML iconHTML  

    Human identification at a distance has recently gained growing interest from computer vision researchers. Gait recognition aims essentially to address this problem by identifying people based on the way they walk. In this paper, a simple but efficient gait recognition algorithm using spatial-temporal silhouette analysis is proposed. For each image sequence, a background subtraction algorithm and a simple correspondence procedure are first used to segment and track the moving silhouettes of a walking figure. Then, eigenspace transformation based on principal component analysis (PCA) is applied to time-varying distance signals derived from a sequence of silhouette images to reduce the dimensionality of the input feature space. Supervised pattern classification techniques are finally performed in the lower-dimensional eigenspace for recognition. This method implicitly captures the structural and transitional characteristics of gait. Extensive experimental results on outdoor image sequences demonstrate that the proposed algorithm has an encouraging recognition performance with relatively low computational cost. View full abstract»

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  • Extraction of features using M-band wavelet packet frame and their neuro-fuzzy evaluation for multitexture segmentation

    Page(s): 1639 - 1644
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (917 KB) |  | HTML iconHTML  

    In this paper, we propose a scheme for segmentation of multitexture images. The methodology involves extraction of texture features using an overcomplete wavelet decomposition scheme called discrete M-band wavelet packet frame (DMbWPF). This is followed by the selection of important features using a neuro-fuzzy algorithm under unsupervised learning. A computationally efficient search procedure is developed for finding the optimal basis based on some maximum criterion of textural measures derived from the statistical parameters for each of the subbands. The superior discriminating capability of the extracted features for segmentation of various texture images over those obtained by several existing methods is established. View full abstract»

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  • The CMU pose, illumination, and expression database

    Page(s): 1615 - 1618
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1512 KB) |  | HTML iconHTML  

    In the Fall of 2000, we collected a database of more than 40,000 facial images of 68 people. Using the Carnegie Mellon University 3D Room, we imaged each person across 13 different poses, under 43 different illumination conditions, and with four different expressions. We call this the CMU pose, illumination, and expression (PIE) database. We describe the imaging hardware, the collection procedure, the organization of the images, several possible uses, and how to obtain the database. View full abstract»

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  • Author index

    Page(s): 1645 - 1650
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    Freely Available from IEEE
  • Subject index

    Page(s): 1650 - 1664
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    Freely Available from IEEE

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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.

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David A. Forsyth
University of Illinois