Face recognition using an NNSRM classifier in LDA subspace

NNSRM is an implementation of the structural risk minimization (SRM) principle using the nearest neighbor (NN) rule, and linear discriminant analysis (LDA) is a dimension-reducing method, which is usually used in classifications. This paper combines the two methods for face recognition. We first project the face images into a PCA subspace, then project the results into a much lower-dimensional LDA subspace, and then use an NNSRM classifier to recognize them in the LDA subspace. Experimental results demonstrate that the combined method can achieve a better performance than NN by selecting different distances and a comparable performance with SVM but costing less computational time.

Motion estimation based color transfer and its application to color video compression

In this paper a novel scheme for color video compression using color transfer technique is proposed. Towards this, a new color transfer mechanism for video using motion estimation is presented. Encoder and decoder architectures for the proposed compression scheme are also presented. In this scheme, compression is achieved by firstly discarding chrominance information for all but selected reference frames and then using motion prediction and discrete cosine transform (DCT) based quantization. At decompression stage, the luminance-only frames are colored using chrominance information from the reference frames applying the proposed color transfer technique. To integrate color transfer mechanism with hybrid compression scheme a new color transfer protocol is defined. Both compression scheme and color transfer work in YCbCr color space.

Semi-supervised discriminative classification with application to tumorous tissues segmentation of MR brain images

Due to the large data size of 3D MR brain images and the blurry boundary of the pathological tissues, tumor segmentation work is difficult. This paper introduces a discriminative classification algorithm for semi-automated segmentation of brain tumorous tissues. The classifier uses interactive hints to obtain models to classify normal and tumor tissues. A non-parametric Bayesian Gaussian random field in the semi-supervised mode is implemented. Our approach uses both labeled data and a subset of unlabeled data sampling from 2D/3D images for training the model. Fast algorithm is also developed. Experiments show that our approach produces satisfactory segmentation results comparing to the manually labeled results by experts.

Human action recognition in videos based on the Transferable Belief Model

This paper focuses on human behavior recognition where the main problem is to bridge the semantic gap between the analogue observations of the real world and the symbolic world of human interpretation. For that, a fusion architecture based on the Transferable Belief Model framework is proposed and applied to action recognition of an athlete in video sequences of athletics meeting with moving camera. Relevant features are extracted from videos, based on both the camera motion analysis and the tracking of particular points on the athlete’s silhouette. Some models of interpretation are used to link the numerical features to the symbols to be recognized, which are running, jumping and falling actions. A Temporal Belief Filter is then used to improve the robustness of action recognition. The proposed approach demonstrates good performance when tested on real videos of athletics sports videos (high jumps, pole vaults, triple jumps and long jumps) acquired by a moving camera and different view angles. The proposed system is also compared to Bayesian Networks.

Periodicity and stability issues of a <Emphasis Type="Italic">chaotic</Emphasis> pattern recognition neural network

Traditional pattern recognition (PR) systems work with the model that the object to be recognized is characterized by a set of features, which are treated as the inputs. In this paper, we propose a new model for PR, namely one that involves chaotic neural networks (CNNs). To achieve this, we enhance the basic model proposed by Adachi (Neural Netw 10:83–98, 1997), referred to as Adachi’s Neural Network (AdNN), which though dynamic, is not chaotic. We demonstrate that by decreasing the multiplicity of the eigenvalues of the AdNN’s control system, we can effectively drive the system into chaos. We prove this result here by eigenvalue computations and the evaluation of the Lyapunov exponent. With this premise, we then show that such a Modified AdNN (M-AdNN) has the desirable property that it recognizes various input patterns. The way that this PR is achieved is by the system essentially sympathetically “resonating” with a finite periodicity whenever these samples (or their reasonable resemblances) are presented. In this paper, we analyze the M-AdNN for its periodicity, stability and the length of the transient phase of the retrieval process. The M-AdNN has been tested for Adachi’s dataset and for a real-life PR problem involving numerals. We believe that this research also opens a host of new research avenues. Research partially supported by the Natural Sciences and Engineering Research Council of Canada.

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Distributional-based texture classification using non-parametric statistics

Texture classification is an important problem in image analysis. In the present study, an efficient strategy for classifying texture images is introduced and examined within a distributional-statistical framework. Our approach incorporates the multivariate Wald–Wolfowitz test (WW-test), a non-parametric statistical test that measures the similarity between two different sets of multivariate data, which is utilized here for comparing texture distributions. By summarizing the texture information using standard feature extraction methodologies, the similarity measure provides a comprehensive estimate of the match between different images based on graph theory. The proposed “distributional metric” is shown to handle efficiently the texture-space dimensionality and the limited sample size drawn from a given image. The experimental results, from the application on a typical texture database, clearly demonstrate the effectiveness of our approach and its superiority over other well-established texture distribution (dis)similarity metrics. In addition, its performance is used to evaluate several approaches for texture representation. Even though the classification results are obtained on grayscale images, a direct extension to color-based ones can be straightforward.

Hierarchical clustering of subpopulations with a dissimilarity based on the likelihood ratio statistic: application to clustering massive data sets

The problem of clustering subpopulations on the basis of samples is considered within a statistical framework: a distribution for the variables is assumed for each subpopulation and the dissimilarity between any two populations is defined as the likelihood ratio statistic which compares the hypothesis that the two subpopulations differ in the parameter of their distributions to the hypothesis that they do not. A general algorithm for the construction of a hierarchical classification is described which has the important property of not having inversions in the dendrogram. The essential elements of the algorithm are specified for the case of well-known distributions (normal, multinomial and Poisson) and an outline of the general parametric case is also discussed. Several applications are discussed, the main one being a novel approach to dealing with massive data in the context of a two-step approach. After clustering the data in a reasonable number of ‘bins’ by a fast algorithm such as k-Means, we apply a version of our algorithm to the resulting bins. Multivariate normality for the means calculated on each bin is assumed: this is justified by the central limit theorem and the assumption that each bin contains a large number of units, an assumption generally justified when dealing with truly massive data such as currently found in modern data analysis. However, no assumption is made about the data generating distribution.

Reverse engineering the way humans rank textures

We argue that in order to understand which features are used by humans to group textures, one must start by computing thousands of features of diverse nature, and select from those features those that allow the reproduction of perceptual groups or perceptual ranking created by humans. We use the Trace transform to produce such features here. We compare these features with those produced from the co-occurrence matrix and its variations. We show that when one is not interested in reproducing human behaviour, the elements of the co-occurrence matrix used as features perform best in terms of texture classification accuracy. However, these features cannot be “trained” or “selected” to imitate human ranking, while the features produced from the Trace transform can. We attribute this to the diverse nature of the features computed from the Trace transform.

The aspect Bernoulli model: multiple causes of presences and absences

We present a probabilistic multiple cause model for the analysis of binary (0–1) data. A distinctive feature of the aspect Bernoulli (AB) model is its ability to automatically detect and distinguish between “true absences” and “false absences” (both of which are coded as 0 in the data), and similarly, between “true presences” and “false presences” (both of which are coded as 1). This is accomplished by specific additive noise components which explicitly account for such non-content bearing causes. The AB model is thus suitable for noise removal and data explanatory purposes, including omission/addition detection. An important application of AB that we demonstrate is data-driven reasoning about palaeontological recordings. Additionally, results on recovering corrupted handwritten digit images and expanding short text documents are also given, and comparisons to other methods are demonstrated and discussed.

A multi-scale template method for shape detection with bio-medical applications

In this paper we present a novel methodology based on non-parametric deformable prototype templates for reconstructing the outline of a shape from a degraded image. Our method is versatile and fast and has the potential to provide an automatic procedure for classifying pathologies. We test our approach on synthetic and real data from a variety of medical and biological applications. In these studies it is important to reconstruct accurately the shape of the object under investigation from very noisy data. Here we assume that we have some prior knowledge about the object outline represented by a prototype shape. Our procedure deforms this shape by means of non-affine transformations and the contour is reconstructed by minimizing a newly developed objective function that depends on the transformation parameters. We introduce an iterative template deformation procedure in which the scale of the deformation decreases as the algorithm proceeds. We compare our results with those from a Gaussian Mixture Model segmentation and two state-of-the-art Level Set methods. This comparison shows that the proposed procedure performs consistently well on both real and simulated data. As a by-product we develop a new filter that recovers the connectivity of a shape.

Quadtree-based eigendecomposition for pose estimation in the presence of occlusion and background clutter

Eigendecomposition-based techniques are popular for a number of computer vision problems, e.g., object and pose estimation, because they are purely appearance based and they require few on-line computations. Unfortunately, they also typically require an unobstructed view of the object whose pose is being detected. The presence of occlusion and background clutter precludes the use of the normalizations that are typically applied and significantly alters the appearance of the object under detection. This work presents an algorithm that is based on applying eigendecomposition to a quadtree representation of the image dataset used to describe the appearance of an object. This allows decisions concerning the pose of an object to be based on only those portions of the image in which the algorithm has determined that the object is not occluded. The accuracy and computational efficiency of the proposed approach is evaluated on 16 different objects with up to 50% of the object being occluded and on images of ships in a dockyard.

Improvement of causal analysis using multivariate statistical process control

Statistical process control (SPC) is a conventional means of monitoring software processes and detecting related problems, where the causes of detected problems can be identified using causal analysis. Determining the actual causes of reported problems requires significant effort due to the large number of possible causes. This study presents an approach to detect problems and identify the causes of problems using multivariate SPC. This proposed method can be applied to monitor multiple measures of software process simultaneously. The measures which are detected as the major impacts to the out-of-control signals can be used to identify the causes where the partial least squares (PLS) and statistical hypothesis testing are utilized to validate the identified causes of problems in this study. The main advantage of the proposed approach is that the correlated indices can be monitored simultaneously to facilitate the causal analysis of a software process.

Optimizing two-pass connected-component labeling algorithms

We present two optimization strategies to improve connected-component labeling algorithms. Taking together, they form an efficient two-pass labeling algorithm that is fast and theoretically optimal. The first optimization strategy reduces the number of neighboring pixels accessed through the use of a decision tree, and the second one streamlines the union-find algorithms used to track equivalent labels. We show that the first strategy reduces the average number of neighbors accessed by a factor of about 2. We prove our streamlined union-find algorithms have the same theoretical optimality as the more sophisticated ones in literature. This result generalizes an earlier one on using union-find in labeling algorithms by Fiorio and Gustedt (Theor Comput Sci 154(2):165–181, 1996). In tests, the new union-find algorithms improve a labeling algorithm by a factor of 4 or more. Through analyses and experiments, we demonstrate that our new two-pass labeling algorithm scales linearly with the number of pixels in the image, which is optimal in computational complexity theory. Furthermore, the new labeling algorithm outperforms the published labeling algorithms irrespective of test platforms. In comparing with the fastest known labeling algorithm for two-dimensional (2D) binary images called contour tracing algorithm, our new labeling algorithm is up to ten times faster than the contour tracing program distributed by the original authors.

FRCT: fuzzy-rough classification trees

Using fuzzy-rough hybrids, we have proposed a measure to quantify the functional dependency of decision attribute(s) on condition attribute(s) within fuzzy data. We have shown that the proposed measure of dependency degree is a generalization of the measure proposed by Pawlak for crisp data. In this paper, this new measure of dependency degree has been encapsulated into the decision tree generation mechanism to produce fuzzy-rough classification trees (FRCT); efficient, top-down, multi-class decision tree structures geared to solving classification problems from feature-based learning examples. The developed FRCT generation algorithm has been applied to 16 real-world benchmark datasets. It is experimentally compared with the five fuzzy decision tree generation algorithms reported so far, and the rough decomposition tree algorithm. Comparison has been made in terms of number of rules, average training time, and classification accuracy. Experimental results show that the proposed algorithm to generate FRCT outperforms existing fuzzy decision tree generation techniques and rough decomposition tree induction algorithm.

A multi-stage classification system for detecting intrusions in computer networks

A serial multi-stage classification system for facing the problem of intrusion detection in computer networks is proposed. The whole decision process is organized into successive stages, each one using a set of features tailored for recognizing a specific attack category. All the stages employ suitable criteria for estimating the reliability of the performed classification, so that, in case of uncertainty, information related to a possible attack are only logged for further processing, without raising an alert for the system manager. This permits to reduce the number of false alarms. On the other hand, in order to keep low the number of missed detections, the proposed system declares a connection as normal traffic only if all the stages do not detect an attack. The proposed multi-stage intrusion detection system has been tested on three different services (http, telnet and ftp) of a standard database used for benchmarking intrusion detection systems and also on real network traffic data. The experimental analysis highlights the effectiveness of the approach: the proposed system behaves significantly better than other multiple classifier systems performing classification in a single stage.

The applicability analysis of ISO/IEC 9126 series of standards in telecom sector: the example of softswitch category of software

A software application called a softswitch is considered within the telecom industry as critical for the proper operation of most telecommunication systems. This paper presents the results of the research for a solution for methodically evaluating the internal quality of a softswitch dedicated to developers and acquirers alike. The resulting solution is designed in form of a prioritized two-part evaluation grid containing measurements of internal quality attributes of software as defined by the technical report ISO/IEC 9126-3. The elaboration of such a grid had the basic objective of giving developers and acquirers of a softswitch the tool to evaluate its internal quality when being purchased or developed in order to assess its design and code prior to its installation. This document also presents the applied research methodology, the proposed solution and recommendations for its use.

The 2007 IEEE CEC simulated car racing competition

This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process, we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competition.

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A New Learning Algorithm for Optimal Stopping

A linear programming formulation of the optimal stopping problem for Markov decision processes is approximated using linear function approximation. Using this formulation, a reinforcement learning scheme based on a primal-dual method and incorporating a sampling device called ‘split sampling’ is proposed and analyzed. An illustrative example from option pricing is also included.

The Effects of Navigation Sensors and Spatial Road Network Data Quality on the Performance of Map Matching Algorithms

Map matching algorithms are utilised to support the navigation module of advanced transport telematics systems. The objective of this paper is to develop a framework to quantify the effects of spatial road network data and navigation sensor data on the performance of map matching algorithms. Three map matching algorithms are tested with different spatial road network data (map scale 1:1,250; 1:2,500 and 1:50,000) and navigation sensor data (global positioning system (GPS) and GPS augmented with deduced reckoning) in order to quantify their performance. The algorithms are applied to different road networks of varying complexity. The performance of the algorithms is then assessed for a suburban road network using high precision positioning data obtained from GPS carrier phase observables. The results show that there are considerable effects of spatial road network data on the performance of map matching algorithms. For an urban road network, the results suggest that both the quality of spatial road network data and the type of navigation system affect the link identification performance of map matching algorithms.

An information retrieval process to aid in the analysis of code clones

The advent of new static analysis tools has automated the searching for code clones, which are duplicated or similar code fragments in a program. However, clone detection tools can report many clones if the source code that is being searched is large. Programmers may have difficulty comprehending the extensive results from the detection tool, which may inhibit the ability to maintain the identified clones. Latent Semantic Indexing (LSI) is an information retrieval technique that attempts to find relationships in a corpus based on the analysis of the documents in the corpus and the terms in the documents. In this paper, LSI is used to cluster clone classes that have been identified initially by a clone detection tool. The goal of this paper is to detect trends and associations among the clustered clone classes and determine if they provide further comprehension to assist in the maintenance of clones. Experimental evaluation of the approach is reported from a sequence of tools that are chained together to perform an analysis of clones detected in the Microsoft Windows NT kernel source code.