Approaches to working in high-dimensional data spaces: gene expression microarrays

This review provides a focused summary of the implications of high-dimensional data spaces produced by gene expression microarrays for building better models of cancer diagnosis, prognosis, and therapeutics. We identify the unique challenges posed by high dimensionality to highlight methodological problems and discuss recent methods in predictive classification, unsupervised subclass discovery, and marker identification

Block-wise motion detection using compressive imaging system

A block-wise motion detection strategy based on compressive imaging, also referred to as feature-specific imaging (FSI), is described in this work. A mixture of Gaussian distributions is used to model the background in a scene. Motion is detected in individual object blocks using feature measurements. Gabor, Hadamard binary and random binary features are studied. Performance of motion detection methods using pixel-wise measurements is analyzed and serves as a baseline for comparison with motion detection techniques based on compressive imaging. ROC (Receiver Operation Characteristic) curves and AUC (Area Under Curve) metrics are used to quantify the algorithm performance. Because a FSI system yields a larger measurement SNR (Signal-to-Noise Ratio) than a traditional system, motion detection methods based on the FSI system have better performance. We show that motion detection algorithms using Hadamard and random binary features in a FSI system yields AUC values of 0.978 and 0.969 respectively. The pixel-based methods are only able to achieve a lower AUC value of 0.627. © 2010 Elsevier B.V. All rights reserved.postprin

Unsupervised amplitude and texture based classification of SAR images with multinomial latent model

We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images for classification purpose. We use Nakagami density to model the class amplitudes and a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error to model the textures of the classes. A non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. The Classification Expectation-Maximization (CEM) algorithm is performed to estimate the class parameters and to classify the pixels. We resort to Integrated Classification Likelihood (ICL) criterion to determine the number of classes in the model. We obtained some classification results of water, land and urban areas in both supervised and unsupervised cases on TerraSAR-X, as well as COSMO-SkyMed data

Aco-based feature selection algorithm for classification

Dataset with a small number of records but big number of attributes represents a phenomenon called “curse of dimensionality”. The classification of this type of dataset requires Feature Selection (FS) methods for the extraction of useful information. The modified graph clustering ant colony optimisation (MGCACO) algorithm is an effective FS method that was developed based on grouping the highly correlated features. However, the MGCACO algorithm has three main drawbacks in producing a features subset because of its clustering method, parameter sensitivity, and the final subset determination. An enhanced graph clustering ant colony optimisation (EGCACO) algorithm is proposed to solve the three (3) MGCACO algorithm problems. The proposed improvement includes: (i) an ACO feature clustering method to obtain clusters of highly correlated features; (ii) an adaptive selection technique for subset construction from the clusters of features; and (iii) a genetic-based method for producing the final subset of features. The ACO feature clustering method utilises the ability of various mechanisms such as intensification and diversification for local and global optimisation to provide highly correlated features. The adaptive technique for ant selection enables the parameter to adaptively change based on the feedback of the search space. The genetic method determines the final subset, automatically, based on the crossover and subset quality calculation. The performance of the proposed algorithm was evaluated on 18 benchmark datasets from the University California Irvine (UCI) repository and nine (9) deoxyribonucleic acid (DNA) microarray datasets against 15 benchmark metaheuristic algorithms. The experimental results of the EGCACO algorithm on the UCI dataset are superior to other benchmark optimisation algorithms in terms of the number of selected features for 16 out of the 18 UCI datasets (88.89%) and the best in eight (8) (44.47%) of the datasets for classification accuracy. Further, experiments on the nine (9) DNA microarray datasets showed that the EGCACO algorithm is superior than the benchmark algorithms in terms of classification accuracy (first rank) for seven (7) datasets (77.78%) and demonstrates the lowest number of selected features in six (6) datasets (66.67%). The proposed EGCACO algorithm can be utilised for FS in DNA microarray classification tasks that involve large dataset size in various application domains

Statistical modeling for simultaneous data clustering, features selection, and outliers rejection

Model-based approaches and in particular finite mixture models are widely used for data clustering, which is a crucial step in several applications of practical importance. Indeed, many pattern recognition, computer vision, and image processing applications can be approached as feature space clustering problems. However, the use of these approaches for complex high-dimensional data presents several challenges such as the presence of many irrelevant features, which may affect the speed, and compromise the accuracy of the used learning algorithm. Another problem is the presence of outliers which potentially influence the resulting model parameters. Generally; clustering, features selection, and outliers detection problems have been approached separately. In this thesis, we propose a unified statistical framework to address the three problems simultaneously. The proposed statistical model partitions a given data set without a priori information about the number of clusters, the saliency of the features, or the number of outliers. We illustrate the performance of our approach using different applications involving synthetic data, real data, and objects shape clustering

An Information Theoretic Approach For Feature Selection And Segmentation In Posterior Fossa Tumors

Posterior Fossa (PF) is a type of brain tumor located in or near brain stem and cerebellum. About 55% - 70 % pediatric brain tumors arise in the posterior fossa, compared with only 15% - 20% of adult tumors. For segmenting PF tumors we should have features to study the characteristics of tumors. In literature, different types of texture features such as Fractal Dimension (FD) and Multifractional Brownian Motion (mBm) have been exploited for measuring randomness associated with brain and tumor tissues structures, and the varying appearance of tissues in magnetic resonance images (MRI). For selecting best features techniques such as neural network and boosting methods have been exploited. However, neural network cannot descirbe about the properties of texture features. We explore methods such as information theroetic methods which can perform feature selection based on properties of texture features. The primary contribution of this dissertation is investigating efficacy of different image features such as intensity, fractal texture, and level - set shape in segmentation of PF tumor for pediatric patients. We explore effectiveness of using four different feature selection and three different segmentation techniques respectively to discriminate tumor regions from normal tissue in multimodal brain MRI. Our research suggest that Kullback - Leibler Divergence (KLD) measure for feature ranking and selection and Expectation Maximization (EM) algorithm for feature fusion and tumor segmentation offer the best performance for the patient data in this study. To improve segmentation accuracy, we need to consider abnormalities such as cyst, edema and necrosis which surround tumors. In this work, we exploit features which describe properties of cyst and technique which can be used to segment it. To achieve this goal, we extend the two class KLD techniques to multiclass feature selection techniques, so that we can effectively select features for tumor, cyst and non tumor tissues. We compute segemntation accuracy by computing number of pixels segemented to total number of pixels for the best features. For automated process we integrate the inhomoheneity correction, feature selection using KLD and segmentation in an integrated EM framework. To validate results we have used similarity coefficients for computing the robustness of segmented tumor and cyst

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Segmentation d'images et suivi d'objets en vidéos approches par estimation, sélection de caractéristiques et contours actifs

Cette thèse aborde deux problèmes parmi les plus importants et les plus complexes dans la vision artificielle, qui sont la segmentation d'images et le suivi d'objets dans les vidéos. Nous proposons plusieurs approches, traitant de ces deux problèmes, qui sont basées sur la modélisation variationnelle (contours actifs) et statistique. Ces approches ont pour but de surmonter différentes limites théoriques et pratiques (algorithmiques) de ces deux problèmes. En premier lieu, nous abordons le problème d'automatisation de la segmentation par contours actifs"ensembles de niveaux", et sa généralisation pour le cas de plusieurs régions. Pour cela, un modèle permettant d'estimer l'information de régions de manière automatique, et adaptative au contenu de l'image, est proposé. Ce modèle n'utilise aucune information a priori sur les régions, et traite également les images de couleur et de texture, avec un nombre arbitraire de régions. Nous introduisons ensuite une approche statistique pour estimer et intégrer la pertinence des caractéristiques et la sémantique dans la segmentation d'objets d'intérêt. En deuxième lieu, nous abordons le problème du suivi d'objets dans les vidéos en utilisant les contours actifs. Nous proposons pour cela deux modèles différents. Le premier suppose que les propriétés photométriques des objets suivis sont invariantes dans le temps, mais le modèle est capable de suivre des objets en présence de bruit, et au milieu de fonds de vidéos non-statiques et encombrés. Ceci est réalisé grâce à l'intégration de l'information de régions, de frontières et de formes des objets suivis. Le deuxième modèle permet de prendre en charge les variations photométriques des objets suivis, en utilisant un modèle statistique adaptatif à l'apparence de ces derniers. Finalement, nous proposons un nouveau modèle statistique, basé sur la Gaussienne généralisée, pour une représentation efficace de données bruitées et de grandes dimensions en segmentation. Ce modèle est utilisé pour assurer la robustesse de la segmentation des images de couleur contenant du bruit, ainsi que des objets en mouvement dans les vidéos (acquises par des caméras statiques) contenant de l'ombrage et/ou des changements soudains d'illumination