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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Issue 3 • Date June 2005

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  • Table of contents

    Page(s): c1 - 389
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  • IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics publication information

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  • Learning semantic scene models from observing activity in visual surveillance

    Page(s): 397 - 408
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    This paper considers the problem of automatically learning an activity-based semantic scene model from a stream of video data. A scene model is proposed that labels regions according to an identifiable activity in each region, such as entry/exit zones, junctions, paths, and stop zones. We present several unsupervised methods that learn these scene elements and present results that show the efficiency of our approach. Finally, we describe how the models can be used to support the interpretation of moving objects in a visual surveillance environment. View full abstract»

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  • Visual learning by coevolutionary feature synthesis

    Page(s): 409 - 425
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    In this paper, a novel genetically inspired visual learning method is proposed. Given the training raster images, this general approach induces a sophisticated feature-based recognition system. It employs the paradigm of cooperative coevolution to handle the computational difficulty of this task. To represent the feature extraction agents, the linear genetic programming is used. The paper describes the learning algorithm and provides a firm rationale for its design. Different architectures of recognition systems are considered that employ the proposed feature synthesis method. An extensive experimental evaluation on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery shows the ability of the proposed approach to attain high recognition performance in different operating conditions. View full abstract»

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  • Evolutionary optimization of a hierarchical object recognition model

    Page(s): 426 - 437
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    A major problem in designing artificial neural networks is the proper choice of the network architecture. Especially for vision networks classifying three-dimensional (3D) objects this problem is very challenging, as these networks are necessarily large and therefore the search space for defining the needed networks is of a very high dimensionality. This strongly increases the chances of obtaining only suboptimal structures from standard optimization algorithms. We tackle this problem in two ways. First, we use biologically inspired hierarchical vision models to narrow the space of possible architectures and to reduce the dimensionality of the search space. Second, we employ evolutionary optimization techniques to determine optimal features and nonlinearities of the visual hierarchy. Here, we especially focus on higher order complex features in higher hierarchical stages. We compare two different approaches to perform an evolutionary optimization of these features. In the first setting, we directly code the features into the genome. In the second setting, in analogy to an ontogenetical development process, we suggest the new method of an indirect coding of the features via an unsupervised learning process, which is embedded into the evolutionary optimization. In both cases the processing nonlinearities are encoded directly into the genome and are thus subject to optimization. The fitness of the individuals for the evolutionary selection process is computed by measuring the network classification performance on a benchmark image database. Here, we use a nearest-neighbor classification approach, based on the hierarchical feature output. We compare the found solutions with respect to their ability to generalize. We differentiate between a first- and a second-order generalization. The first-order generalization denotes how well the vision system, after evolutionary optimization of the features and nonlinearities using a database A, can classify previously unseen test vi- - ews of objects from this database A. As second-order generalization, we denote the ability of the vision system to perform classification on a database B using the features and nonlinearities optimized on database A. We show that the direct feature coding approach leads to networks with a better first-order generalization, whereas the second-order generalization is on an equally high level for both direct and indirect coding. We also compare the second-order generalization results with other state-of-the-art recognition systems and show that both approaches lead to optimized recognition systems, which are highly competitive with recent recognition algorithms. View full abstract»

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  • Visual learning by imitation with motor representations

    Page(s): 438 - 449
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    We propose a general architecture for action (mimicking) and program (gesture) level visual imitation. Action-level imitation involves two modules. The viewpoint transformation (VPT) performs a "rotation" to align the demonstrator's body to that of the learner. The visuo-motor map (VMM) maps this visual information to motor data. For program-level (gesture) imitation, there is an additional module that allows the system to recognize and generate its own interpretation of observed gestures to produce similar gestures/goals at a later stage. Besides the holistic approach to the problem, our approach differs from traditional work in i) the use of motor information for gesture recognition; ii) usage of context (e.g., object affordances) to focus the attention of the recognition system and reduce ambiguities, and iii) use iconic image representations for the hand, as opposed to fitting kinematic models to the video sequence. This approach is motivated by the finding of visuomotor neurons in the F5 area of the macaque brain that suggest that gesture recognition/imitation is performed in motor terms (mirror) and rely on the use of object affordances (canonical) to handle ambiguous actions. Our results show that this approach can outperform more conventional (e.g., pure visual) methods. View full abstract»

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  • Active concept learning in image databases

    Page(s): 450 - 466
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1479 KB) |  | HTML iconHTML  

    Concept learning in content-based image retrieval systems is a challenging task. This paper presents an active concept learning approach based on the mixture model to deal with the two basic aspects of a database system: the changing (image insertion or removal) nature of a database and user queries. To achieve concept learning, we a) propose a new user directed semi-supervised expectation-maximization algorithm for mixture parameter estimation, and b) develop a novel model selection method based on Bayesian analysis that evaluates the consistency of hypothesized models with the available information. The analysis of exploitation versus exploration in the search space helps to find the optimal model efficiently. Our concept knowledge transduction approach is able to deal with the cases of image insertion and query images being outside the database. The system handles the situation where users may mislabel images during relevance feedback. Experimental results on Corel database show the efficacy of our active concept learning approach and the improvement in retrieval performance by concept transduction. View full abstract»

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  • Face detection using spectral histograms and SVMs

    Page(s): 467 - 476
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    We present a face detection method using spectral histograms and support vector machines (SVMs). Each image window is represented by its spectral histogram, which is a feature vector consisting of histograms of filtered images. Using statistical sampling, we show systematically the representation groups face images together; in comparison, commonly used representations often do not exhibit this necessary and desirable property. By using an SVM trained on a set of 4500 face and 8000 nonface images, we obtain a robust classifying function for face and nonface patterns. With an effective illumination-correction algorithm, our system reliably discriminates face and nonface patterns in images under different kinds of conditions. Our method on two commonly used data sets give the best performance among recent face-detection ones. We attribute the high performance to the desirable properties of the spectral histogram representation and good generalization of SVMs. Several further improvements in computation time and in performance are discussed. View full abstract»

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  • Learning from examples in the small sample case: face expression recognition

    Page(s): 477 - 488
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    Example-based learning for computer vision can be difficult when a large number of examples to represent each pattern or object class is not available. In such situations, learning from a small number of samples is of practical value. To study this issue, the task of face expression recognition with a small number of training images of each expression is considered. A new technique based on linear programming for both feature selection and classifier training is introduced. A pairwise framework for feature selection, instead of using all classes simultaneously, is presented. Experimental results compare the method with three others: a simplified Bayes classifier, support vector machine, and AdaBoost. Finally, each algorithm is analyzed and a new categorization of these algorithms is given, especially for learning from examples in the small sample case. View full abstract»

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  • Kernel pooled local subspaces for classification

    Page(s): 489 - 502
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    We investigate the use of subspace analysis methods for learning low-dimensional representations for classification. We propose a kernel-pooled local discriminant subspace method and compare it against competing techniques: kernel principal component analysis (KPCA) and generalized discriminant analysis (GDA) in classification problems. We evaluate the classification performance of the nearest-neighbor rule with each subspace representation. The experimental results using several data sets demonstrate the effectiveness and performance superiority of the kernel-pooled subspace method over competing methods such as KPCA and GDA in some classification problems. View full abstract»

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  • Self-organizing maps for learning the edit costs in graph matching

    Page(s): 503 - 514
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    Although graph matching and graph edit distance computation have become areas of intensive research recently, the automatic inference of the cost of edit operations has remained an open problem. In the present paper, we address the issue of learning graph edit distance cost functions for numerically labeled graphs from a corpus of sample graphs. We propose a system of self-organizing maps (SOMs) that represent the distance measuring spaces of node and edge labels. Our learning process is based on the concept of self-organization. It adapts the edit costs in such a way that the similarity of graphs from the same class is increased, whereas the similarity of graphs from different classes decreases. The learning procedure is demonstrated on two different applications involving line drawing graphs and graphs representing diatoms, respectively. View full abstract»

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  • Self-organizing topological tree for online vector quantization and data clustering

    Page(s): 515 - 526
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    The self-organizing maps (SOM) introduced by Kohonen implement two important operations: vector quantization (VQ) and a topology-preserving mapping. In this paper, an online self-organizing topological tree (SOTT) with faster learning is proposed. A new learning rule delivers the efficiency and topology preservation, which is superior of other structures of SOMs. The computational complexity of the proposed SOTT is O(logN) rather than O(N) as for the basic SOM. The experimental results demonstrate that the reconstruction performance of SOTT is comparable to the full-search SOM and its computation time is much shorter than the full-search SOM and other vector quantizers. In addition, SOTT delivers the hierarchical mapping of codevectors and the progressive transmission and decoding property, which are rarely supported by other vector quantizers at the same time. To circumvent the shortcomings of clustering performance of classical partition clustering algorithms, a hybrid clustering algorithm that fully exploit the online learning and multiresolution characteristics of SOTT is devised. A new linkage metric is proposed which can be updated online to accelerate the time consuming agglomerative hierarchical clustering stage. Besides the enhanced clustering performance, due to the online learning capability, the memory requirement of the proposed SOTT hybrid clustering algorithm is independent of the size of the data set, making it attractive for large database. View full abstract»

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  • A learning-based method for image super-resolution from zoomed observations

    Page(s): 527 - 537
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    We propose a technique for super-resolution imaging of a scene from observations at different camera zooms. Given a sequence of images with different zoom factors of a static scene, we obtain a picture of the entire scene at a resolution corresponding to the most zoomed observation. The high-resolution image is modeled through appropriate parameterization, and the parameters are learned from the most zoomed observation. Assuming a homogeneity of the high-resolution field, the learned model is used as a prior while super-resolving the scene. We suggest the use of either a Markov random field (MRF) or an simultaneous autoregressive (SAR) model to parameterize the field based on the computation one can afford. We substantiate the suitability of the proposed method through a large number of experimentations on both simulated and real data. View full abstract»

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  • Object detection via feature synthesis using MDL-based genetic programming

    Page(s): 538 - 547
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    In this paper, we use genetic programming (GP) to synthesize composite operators and composite features from combinations of primitive operations and primitive features for object detection. The motivation for using GP is to overcome the human experts' limitations of focusing only on conventional combinations of primitive image processing operations in the feature synthesis. GP attempts many unconventional combinations that in some cases yield exceptionally good results. To improve the efficiency of GP and prevent its well-known code bloat problem without imposing severe restriction on the GP search, we design a new fitness function based on minimum description length principle to incorporate both the pixel labeling error and the size of a composite operator into the fitness evaluation process. To further improve the efficiency of GP, smart crossover, smart mutation and a public library ideas are incorporated to identify and keep the effective components of composite operators. Our experiments, which are performed on selected training regions of a training image to reduce the training time, show that compared to normal GP, our GP algorithm finds effective composite operators more quickly and the learned composite operators can be applied to the whole training image and other similar testing images. Also, compared to a traditional region-of-interest extraction algorithm, the composite operators learned by GP are more effective and efficient for object detection. View full abstract»

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  • Evolving binary classifiers through parallel computation of multiple fitness cases

    Page(s): 548 - 555
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    This paper describes two versions of a novel approach to developing binary classifiers, based on two evolutionary computation paradigms: cellular programming and genetic programming. Such an approach achieves high computation efficiency both during evolution and at runtime. Evolution speed is optimized by allowing multiple solutions to be computed in parallel. Runtime performance is optimized explicitly using parallel computation in the case of cellular programming or implicitly taking advantage of the intrinsic parallelism of bitwise operators on standard sequential architectures in the case of genetic programming. The approach was tested on a digit recognition problem and compared with a reference classifier. View full abstract»

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  • A criterion for optimizing kernel parameters in KBDA for image retrieval

    Page(s): 556 - 562
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    A criterion is proposed to optimize the kernel parameters in kernel-based biased discriminant analysis (KBDA) for image retrieval. Kernel parameter optimization is performed by optimizing the kernel space such that the positive images are well clustered while the negative ones are pushed far away from the positives. The proposed criterion measures the goodness of a kernel space, and the optimal kernel parameter set is obtained by maximizing this criterion. Retrieval experiments on two benchmark image databases demonstrate the effectiveness of proposed criterion for KBDA to achieve the best possible performance at the cost of a small fractional computational overhead. View full abstract»

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  • Image transform bootstrapping and its applications to semantic scene classification

    Page(s): 563 - 570
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    The performance of an exemplar-based scene classification system depends largely on the size and quality of its set of training exemplars, which can be limited in practice. In addition, in nontrivial data sets, variations in scene content as well as distracting regions may exist in many testing images to prohibit good matches with the exemplars. Various boosting schemes have been proposed in machine learning, focusing on the feature space. We introduce the novel concept of image-transform bootstrapping using transforms in the image space to address such issues. In particular, three major schemes are described for exploiting this concept to augment training, testing, and both. We have successfully applied it to three applications of increasing difficulty: sunset detection, outdoor scene classification, and automatic image orientation detection. It is shown that appropriate transforms and meta-classification methods can be selected to boost performance according to the domain of the problem and the features/classifier used. View full abstract»

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  • EM in high-dimensional spaces

    Page(s): 571 - 577
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    This paper considers fitting a mixture of Gaussians model to high-dimensional data in scenarios where there are fewer data samples than feature dimensions. Issues that arise when using principal component analysis (PCA) to represent Gaussian distributions inside Expectation-Maximization (EM) are addressed, and a practical algorithm results. Unlike other algorithms that have been proposed, this algorithm does not try to compress the data to fit low-dimensional models. Instead, it models Gaussian distributions in the (N-1)-dimensional space spanned by the N data samples. We are able to show that this algorithm converges on data sets where low-dimensional techniques do not. View full abstract»

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  • Robust fusion of uncertain information

    Page(s): 578 - 586
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    A technique is presented to combine n data points, each available with point-dependent uncertainty, when only a subset of these points come from N≪n sources, where N is unknown. We detect the significant modes of the underlying multivariate probability distribution using a generalization of the nonparametric mean shift procedure. The number of detected modes automatically defines N, while the belonging of a point to the basin of attraction of a mode provides the fusion rule. The robust data fusion algorithm was successfully applied to two computer vision problems: estimating the multiple affine transformations, and range image segmentation. View full abstract»

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  • Recursive three-dimensional model reconstruction based on Kalman filtering

    Page(s): 587 - 592
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    A recursive two-step method to recover structure and motion from image sequences based on Kalman filtering is described in this paper. The algorithm consists of two major steps. The first step is an extended Kalman filter (EKF) for the estimation of the object's pose. The second step is a set of EKFs, one for each model point, for the refinement of the positions of the model features in the three-dimensional (3-D) space. These two steps alternate from frame to frame. The initial model converges to the final structure as the image sequence is scanned sequentially. The performance of the algorithm is demonstrated with both synthetic data and real-world objects. Analytical and empirical comparisons are made among our approach, the interleaved bundle adjustment method, and the Kalman filtering-based recursive algorithm by Azarbayejani and Pentland. Our approach outperformed the other two algorithms in terms of computation speed without loss in the quality of model reconstruction. View full abstract»

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  • A kernel autoassociator approach to pattern classification

    Page(s): 593 - 606
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    Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional nonlinear autoassociation models emphasize searching for the nonlinear representations of input patterns, a kernel autoassociator takes a kernel feature space as the nonlinear manifold, and places emphasis on the reconstruction of input patterns from the kernel feature space. Two methods are proposed to address the reconstruction problem, using linear and multivariate polynomial functions, respectively. We apply the proposed model to novelty detection with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to mclass classification problems including wine recognition, glass recognition, handwritten digit recognition, and face recognition. The experimental results show that, compared with conventional autoassociators and other recognition systems, kernel autoassociators can provide better or comparable performance for concept learning and recognition in various domains. View full abstract»

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  • Eliminating false matches for the projective registration of free-form surfaces with small translational motions

    Page(s): 607 - 624
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    In this paper, we make a detailed study of two rigid-motion constraints. The importance of these two constraints is twofold: first, they reveal the inherent relationship between the three-dimensional-two-dimensional (3-D-2-D) point correspondences and the motion parameters of interest; second, they can be used to measure the traditional ICP criterion established point match qualities based on which different point matches can be compared and relatively good point matches can be selected for motion-parameter update in the projective registration of free-form surfaces subject to small translational motions. The experimental results based on both synthetic data and real images have shown that the rigid motion constraints are powerful in evaluating the possible 3-D-2-D point matches established by the traditional ICP criterion, thus achieving encouraging projective registration results. View full abstract»

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  • Sample-sort simulated annealing

    Page(s): 625 - 632
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    A simulated annealing (SA) algorithm called Sample-Sort that is artificially extended across an array of samplers is proposed. The sequence of temperatures for a serial SA algorithm is replaced with an array of samplers operating at static temperatures and the single stochastic sampler is replaced with a set of samplers. The set of samplers uses a biased generator to sample the same distribution of a serial SA algorithm to maintain the same convergence property. Sample-Sort was compared to SA by applying both to a set of global optimization problems and found to be comparable if the number of iterations per sampler was sufficient. If the evaluation phase dominates the computational requirements, Sample-Sort could take advantage of parallel processing. View full abstract»

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Aims & Scope

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics focuses on cybernetics, including communication and control across humans, machines and organizations at the structural or neural level

 

This Transaction ceased production in 2012. The current retitled publication is IEEE Transactions on Cybernetics.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Dr. Eugene Santos, Jr.
Thayer School of Engineering
Dartmouth College