Scene Analysis Using Scale Invariant Feature Extraction and Probabilistic Modeling

Conventional pattern recognition systems have two components: feature analysis and pattern classification. For any object in an image, features could be considered as the major characteristic of the object either for object recognition or object tracking purpose. Features extracted from a training image, can be used to identify the object when attempting to locate the object in a test image containing many other objects. To perform reliable scene analysis, it is important that the features extracted from the training image are detectable even under changes in image scale, noise and illumination. Scale invariant feature has wide applications such as image classification, object recognition and object tracking in the image processing area. In this thesis, color feature and SIFT (scale invariant feature transform) are considered to be scale invariant feature. The classification, recognition and tracking result were evaluated with novel evaluation criterion and compared with some existing methods. I also studied different types of scale invariant feature for the purpose of solving scene analysis problems. I propose probabilistic models as the foundation of analysis scene scenario of images. In order to differential the content of image, I develop novel algorithms for the adaptive combination for multiple features extracted from images. I demonstrate the performance of the developed algorithm on several scene analysis tasks, including object tracking, video stabilization, medical video segmentation and scene classification

A perceptual learning model to discover the hierarchical latent structure of image collections

Biology has been an unparalleled source of inspiration for the work of researchers in several scientific and engineering fields including computer vision. The starting point of this thesis is the neurophysiological properties of the human early visual system, in particular, the cortical mechanism that mediates learning by exploiting information about stimuli repetition. Repetition has long been considered a fundamental correlate of skill acquisition andmemory formation in biological aswell as computational learning models. However, recent studies have shown that biological neural networks have differentways of exploiting repetition in forming memory maps. The thesis focuses on a perceptual learning mechanism called repetition suppression, which exploits the temporal distribution of neural activations to drive an efficient neural allocation for a set of stimuli. This explores the neurophysiological hypothesis that repetition suppression serves as an unsupervised perceptual learning mechanism that can drive efficient memory formation by reducing the overall size of stimuli representation while strengthening the responses of the most selective neurons. This interpretation of repetition is different from its traditional role in computational learning models mainly to induce convergence and reach training stability, without using this information to provide focus for the neural representations of the data. The first part of the thesis introduces a novel computational model with repetition suppression, which forms an unsupervised competitive systemtermed CoRe, for Competitive Repetition-suppression learning. The model is applied to generalproblems in the fields of computational intelligence and machine learning. Particular emphasis is placed on validating the model as an effective tool for the unsupervised exploration of bio-medical data. In particular, it is shown that the repetition suppression mechanism efficiently addresses the issues of automatically estimating the number of clusters within the data, as well as filtering noise and irrelevant input components in highly dimensional data, e.g. gene expression levels from DNA Microarrays. The CoRe model produces relevance estimates for the each covariate which is useful, for instance, to discover the best discriminating bio-markers. The description of the model includes a theoretical analysis using Huber’s robust statistics to show that the model is robust to outliers and noise in the data. The convergence properties of themodel also studied. It is shown that, besides its biological underpinning, the CoRe model has useful properties in terms of asymptotic behavior. By exploiting a kernel-based formulation for the CoRe learning error, a theoretically sound motivation is provided for the model’s ability to avoid local minima of its loss function. To do this a necessary and sufficient condition for global error minimization in vector quantization is generalized by extending it to distance metrics in generic Hilbert spaces. This leads to the derivation of a family of kernel-based algorithms that address the local minima issue of unsupervised vector quantization in a principled way. The experimental results show that the algorithm can achieve a consistent performance gain compared with state-of-the-art learning vector quantizers, while retaining a lower computational complexity (linear with respect to the dataset size). Bridging the gap between the low level representation of the visual content and the underlying high-level semantics is a major research issue of current interest. The second part of the thesis focuses on this problem by introducing a hierarchical and multi-resolution approach to visual content understanding. On a spatial level, CoRe learning is used to pool together the local visual patches by organizing them into perceptually meaningful intermediate structures. On the semantical level, it provides an extension of the probabilistic Latent Semantic Analysis (pLSA) model that allows discovery and organization of the visual topics into a hierarchy of aspects. The proposed hierarchical pLSA model is shown to effectively address the unsupervised discovery of relevant visual classes from pictorial collections, at the same time learning to segment the image regions containing the discovered classes. Furthermore, by drawing on a recent pLSA-based image annotation system, the hierarchical pLSA model is extended to process and representmulti-modal collections comprising textual and visual data. The results of the experimental evaluation show that the proposed model learns to attach textual labels (available only at the level of the whole image) to the discovered image regions, while increasing the precision/ recall performance with respect to flat, pLSA annotation model

A combined method based on CNN architecture for variation-resistant facial recognition

Identifying individuals from a facial image is a technique that forms part of computer vision and is used in various fields such as security, digital biometrics, smartphones, and banking. However, it can prove difficult due to the complexity of facial structure and the presence of variations that can affect the results. To overcome this difficulty, in this paper, we propose a combined approach that aims to improve the accuracy and robustness of facial recognition in the presence of variations. To this end, two datasets (ORL and UMIST) are used to train our model. We then began with the image pre-processing phase, which consists in applying a histogram equalization operation to adjust the gray levels over the entire image surface to improve quality and enhance the detection of features in each image. Next, the least important features are eliminated from the images using the Principal Component Analysis (PCA) method. Finally, the pre-processed images are subjected to a neural network architecture (CNN) consisting of multiple convolution layers and fully connected layers. Our simulation results show a high performance of our approach, with accuracy rates of up to 99.50% for the ORL dataset and 100% for the UMIST dataset

Investigations on number selection for finite mixture models and clustering analysis.

by Yiu Ming Cheung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 92-99).Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.1.1 --- Bayesian YING-YANG Learning Theory and Number Selec- tion Criterion --- p.5Chapter 1.2 --- General Motivation --- p.6Chapter 1.3 --- Contributions of the Thesis --- p.6Chapter 1.4 --- Other Related Contributions --- p.7Chapter 1.4.1 --- A Fast Number Detection Approach --- p.7Chapter 1.4.2 --- Application of RPCL to Prediction Models for Time Series Forecasting --- p.7Chapter 1.4.3 --- Publications --- p.8Chapter 1.5 --- Outline of the Thesis --- p.8Chapter 2 --- Open Problem: How Many Clusters? --- p.11Chapter 3 --- Bayesian YING-YANG Learning Theory: Review and Experiments --- p.17Chapter 3.1 --- Briefly Review of Bayesian YING-YANG Learning Theory --- p.18Chapter 3.2 --- Number Selection Criterion --- p.20Chapter 3.3 --- Experiments --- p.23Chapter 3.3.1 --- Experimental Purposes and Data Sets --- p.23Chapter 3.3.2 --- Experimental Results --- p.23Chapter 4 --- Conditions of Number Selection Criterion --- p.39Chapter 4.1 --- Alternative Condition of Number Selection Criterion --- p.40Chapter 4.2 --- Conditions of Special Hard-cut Criterion --- p.45Chapter 4.2.1 --- Criterion Conditions in Two-Gaussian Case --- p.45Chapter 4.2.2 --- Criterion Conditions in k*-Gaussian Case --- p.59Chapter 4.3 --- Experimental Results --- p.60Chapter 4.3.1 --- Purpose and Data Sets --- p.60Chapter 4.3.2 --- Experimental Results --- p.63Chapter 4.4 --- Discussion --- p.63Chapter 5 --- Application of Number Selection Criterion to Data Classification --- p.80Chapter 5.1 --- Unsupervised Classification --- p.80Chapter 5.1.1 --- Experiments --- p.81Chapter 5.2 --- Supervised Classification --- p.82Chapter 5.2.1 --- RBF Network --- p.85Chapter 5.2.2 --- Experiments --- p.86Chapter 6 --- Conclusion and Future Work --- p.89Chapter 6.1 --- Conclusion --- p.89Chapter 6.2 --- Future Work --- p.90Bibliography --- p.92Chapter A --- A Number Detection Approach for Equal-and-Isotropic Variance Clusters --- p.100Chapter A.1 --- Number Detection Approach --- p.100Chapter A.2 --- Demonstration Experiments --- p.102Chapter A.3 --- Remarks --- p.105Chapter B --- RBF Network with RPCL Approach --- p.106Chapter B.l --- Introduction --- p.106Chapter B.2 --- Normalized RBF net and Extended Normalized RBF Net --- p.108Chapter B.3 --- Demonstration --- p.110Chapter B.4 --- Remarks --- p.113Chapter C --- Adaptive RPCL-CLP Model for Financial Forecasting --- p.114Chapter C.1 --- Introduction --- p.114Chapter C.2 --- Extraction of Input Patterns and Outputs --- p.115Chapter C.3 --- RPCL-CLP Model --- p.116Chapter C.3.1 --- RPCL-CLP Architecture --- p.116Chapter C.3.2 --- Training Stage of RPCL-CLP --- p.117Chapter C.3.3 --- Prediction Stage of RPCL-CLP --- p.122Chapter C.4 --- Adaptive RPCL-CLP Model --- p.122Chapter C.4.1 --- Data Pre-and-Post Processing --- p.122Chapter C.4.2 --- Architecture and Implementation --- p.122Chapter C.5 --- Computer Experiments --- p.125Chapter C.5.1 --- Data Sets and Experimental Purpose --- p.125Chapter C.5.2 --- Experimental Results --- p.126Chapter C.6 --- Conclusion --- p.134Chapter D --- Publication List --- p.135Chapter D.1 --- Publication List --- p.13

A neural-visualization IDS for honeynet data

Neural intelligent systems can provide a visualization of the network traffic for security staff, in order to reduce the widely known high false-positive rate associated with misuse-based Intrusion Detection Systems (IDSs). Unlike previous work, this study proposes an unsupervised neural models that generate an intuitive visualization of the captured traffic, rather than network statistics. These snapshots of network events are immensely useful for security personnel that monitor network behavior. The system is based on the use of different neural projection and unsupervised methods for the visual inspection of honeypot data, and may be seen as a complementary network security tool that sheds light on internal data structures through visual inspection of the traffic itself. Furthermore, it is intended to facilitate verification and assessment of Snort performance (a well-known and widely-used misuse-based IDS), through the visualization of attack patterns. Empirical verification and comparison of the proposed projection methods are performed in a real domain, where two different case studies are defined and analyzedRegional Government of Gipuzkoa, the Department of Research, Education and Universities of the Basque Government, and the Spanish Ministry of Science and Innovation (MICINN) under projects TIN2010-21272-C02-01 and CIT-020000-2009-12 (funded by the European Regional Development Fund). This work was also supported in the framework of the IT4Innovations Centre of Excellence project, reg. no. CZ.1.05/1.1.00/02.0070 supported by the Operational Program 'Research and Development for Innovations' funded through the Structural Funds of the European Union and the state budget of the Czech RepublicElectronic version of an article published as International Journal of Neural Systems, Volume 22, Issue 02, April 2012 10.1142/S0129065712500050 ©copyright World Scientific Publishing Company http://www.worldscientific.com/worldscinet/ijn

Internetsuche und Neuronale Netze: Stand der Technik

Veröffentlichung des Wilhelm-Schickard-Institut für Informatik Universität Tübinge

Optimisation de trajectoires d'outil pour l'usinage de surfaces gauches

Cette thèse a pour objet la définition de méthodes efficaces pour l'optimisation du temps d'usinage en bout et par zones des surfaces gauches sur les machines 3-axes et 3+2-axes, sous contrainte de qualité (hauteur de crête maximale) en utilisant la stratégie plans parallèles. Le premier chapitre présente les prérequis à savoir sur le problème d'usinage, ainsi que les notions de bases nécessaires à la compréhension de la suite de ce travail. Le second chapitre est consacré à une nouvelle méthodologie d'approximation des surfaces gauches par des plans, à l'aide de l'Analyse en Composantes Principales (ACP). Dans le troisième chapitre , le partitionnement des surfaces gauches est traité. Une nouvelle métrique est introduite permettant prendre en compte le comportement cinématique de la machine, de plus différents algorithmes de partitionnement sont testés et comparés. Dans le dernier chapitre, l'usinage de surfaces gauches est formulé comme un problème d'optimisation où on cherche à minimiser le temps d'usinage sous contrainte de hauteur de crête. Une formulation originale intégrant les étapes de partitionnement et d'usinage est proposée. Le problème d'optimisation ainsi obtenu est de type boite noire et résolu par le logiciel NOMAD. Une approche heuristique combinant ACP et optimisation est également proposée. Cette approche permet d'obtenir des gains considérables par rapport aux résultats que nous avons obtenus jusqu'ici et ceux de la littérature.The objective of this thesis is to define efficient methods for optimization of free-form surfaces machining time. Specifically, 3-axis and 3+2-axis by zone end-milling, using parallel-planes strategy are considered under quality constraints (maximum scallop height). The first chapter presents the prerequisites to know about the machining problem, as well as the basic notions necessary to understand the rest of this work. The second chapter is devoted to a new methodology of approximation of free-form surfaces by planes, using Principal Component Analysis (PCA). In the third chapter, the clustering of free-form surfaces is treated. A new metric is introduced to take into account the kinematic behavior of the machine, also, different clustering algorithms are tested and compared. In the last chapter, the machining of free-form surfaces is formulated as an optimization problem where we seek to minimize the machining time under the scallop height constraint. An original formulation integrating the clustering and machining steps is proposed. The resulting optimization problem is a black-box problem solved by the NOMAD software. A heuristic approach combining PCA and optimization is also proposed, this approach allows to obtain considerable gains compared to the results we have obtained so far and those from the literature

A study of distance-based machine learning algorithms

Distance-based algorithms are machine learning algorithms that classify queries by computing distances between these queries and a number of internally stored exemplars. Exemplars that are closest to the query have the largest in uence on the classi cation assigned to the query. Two speci c distance-based algorithms, the nearest neighbor algorithm and the nearest-hyperrectangle algorithm, are studied in detail. It is shown that the k-nearest neighbor algorithm (kNN) outperforms the rst- nearest neighbor algorithm only under certain conditions. Data sets must contain moderate amounts of noise. Training examples from the di erent classes must belong to clusters that allow an increase in the value of k without reaching into clusters of other classes. Methods for choosing the value of k for kNN are investigated. It is shown that one-fold cross-validation on a restricted number of values for k su ces for best performance. It is also shown that for best performance the votes of the k-nearest neighbors of a query should be weighted in inverse proportion to their distances from the query. Principal component analysis is shown to reduce the number of relevant dimen- sions substantially in several domains. Two methods for learning feature weights for a weighted Euclidean distance metric are proposed. These methods improve the performance of kNN and NN in a variety of domains. The nearest-hyperrectangle algorithm (NGE) is found to give predictions that are substantially inferior to those given by kNN in a variety of domains. Experiments performed to understand this inferior performance led to the discovery of several improvements to NGE. Foremost of these is BNGE, a batch algorithm that avoids construction of overlapping hyperrectangles from di erent classes. Although it is generally superior to NGE, BNGE is still signi cantly inferior to kNN in a variety of domains. Hence, a hybrid algorithm (KBNGE), that uses BNGE in parts of the input space that can be represented by a single hyperrectangle and kNN otherwise, is introduced. The primary contributions of this dissertation are (a) several improvements to existing distance-based algorithms, (b) several new distance-based algorithms, and (c) an experimentally supported understanding of the conditions under which various distance-based algorithms are likely to give good performance

The Geometry of Data: Distance on Data Manifolds

The increasing importance of data in the modern world has created a need for new mathematical techniques to analyze this data. We explore and develop the use of geometry—specifically differential geometry—as a means for such analysis, in two parts. First, we provide a general framework to discover patterns contained in time series data using a geometric framework of assigning distance, clustering, and then forecasting. Second, we attempt to define a Riemannian metric on the space containing the data in order to introduce a notion of distance intrinsic to the data, providing a novel way to probe the data for insight