Robust formulations for clustering-based large-scale classification

Chebyshev-inequality-based convex relaxations of Chance-Constrained Programs (CCPs) are shown to be useful for learning classifiers on massive datasets. In particular, an algorithm that integrates efficient clustering procedures and CCP approaches for computing classifiers on large datasets is proposed. The key idea is to identify high density regions or clusters from individual class conditional densities and then use a CCP formulation to learn a classifier on the clusters. The CCP formulation ensures that most of the data points in a cluster are correctly classified by employing a Chebyshev-inequality-based convex relaxation. This relaxation is heavily dependent on the second-order statistics. However, this formulation and in general such relaxations that depend on the second-order moments are susceptible to moment estimation errors. One of the contributions of the paper is to propose several formulations that are robust to such errors. In particular a generic way of making such formulations robust to moment estimation errors is illustrated using two novel confidence sets. An important contribution is to show that when either of the confidence sets is employed, for the special case of a spherical normal distribution of clusters, the robust variant of the formulation can be posed as a second-order cone program. Empirical results show that the robust formulations achieve accuracies comparable to that with true moments, even when moment estimates are erroneous. Results also illustrate the benefits of employing the proposed methodology for robust classification of large-scale datasets.

Multiobjective discrete particle swarm optimization for multisensor image alignment

A new technique is proposed for multisensor image registration by matching the features using discrete particle swarm optimization (DPSO). The feature points are first extracted from the reference and sensed image using improved Harris corner detector available in the literature. From the extracted corner points, DPSO finds the three corresponding points in the sensed and reference images using multiobjective optimization of distance and angle conditions through objective switching technique. By this, the global best matched points are obtained which are used to evaluate the affine transformation for the sensed image. The performance of the image registration is evaluated and concluded that the proposed approach is efficient.

Integration of speckle de-noising and image segmentation using synthetic aperture radar image for flood extent extraction

Flood is one of the detrimental hydro-meteorological threats to mankind. This compels very efficient flood assessment models. In this paper, we propose remote sensing based flood assessment using Synthetic Aperture Radar (SAR) image because of its imperviousness to unfavourable weather conditions. However, they suffer from the speckle noise. Hence, the processing of SAR image is applied in two stages: speckle removal filters and image segmentation methods for flood mapping. The speckle noise has been reduced with the help of Lee, Frost and Gamma MAP filters. A performance comparison of these speckle removal filters is presented. From the results obtained, we deduce that the Gamma MAP is reliable. The selected Gamma MAP filtered image is segmented using Gray Level Co-occurrence Matrix (GLCM) and Mean Shift Segmentation (MSS). The GLCM is a texture analysis method that separates the image pixels into water and non-water groups based on their spectral feature whereas MSS is a gradient ascent method, here segmentation is carried out using spectral and spatial information. As test case, Kosi river flood is considered in our study. From the segmentation result of both these methods are comprehensively analysed and concluded that the MSS is efficient for flood mapping.

Efficient method of moving shadow detection and vehicle classification

Moving shadow detection and removal from the extracted foreground regions of video frames, aim to limit the risk of misconsideration of moving shadows as a part of moving objects. This operation thus enhances the rate of accuracy in detection and classification of moving objects. With a similar reasoning, the present paper proposes an efficient method for the discrimination of moving object and moving shadow regions in a video sequence, with no human intervention. Also, it requires less computational burden and works effectively under dynamic traffic road conditions on highways (with and without marking lines), street ways (with and without marking lines). Further, we have used scale-invariant feature transform-based features for the classification of moving vehicles (with and without shadow regions), which enhances the effectiveness of the proposed method. The potentiality of the method is tested with various data sets collected from different road traffic scenarios, and its superiority is compared with the existing methods. (C) 2013 Elsevier GmbH. All rights reserved.

Classification of Vehicles Using Magnetic Field Angle Model

This paper presents an efficient approach to the modeling and classification of vehicles using the magnetic signature of the vehicle. A database was created using the magnetic signature collected over a wide range of vehicles(cars). A sensor dependent approach called as Magnetic Field Angle Model is proposed for modeling the obtained magnetic signature. Based on the data model, we present a novel method to extract the feature vector from the magnetic signature. In the classification of vehicles, a linear support vector machine configuration is used to classify the vehicles based on the obtained feature vectors.

Efficient gradient-free simplex method for estimation of optical properties in image-guided diffuse optical tomography

Typical image-guided diffuse optical tomographic image reconstruction procedures involve reduction of the number of optical parameters to be reconstructed equal to the number of distinct regions identified in the structural information provided by the traditional imaging modality. This makes the image reconstruction problem less ill-posed compared to traditional underdetermined cases. Still, the methods that are deployed in this case are same as those used for traditional diffuse optical image reconstruction, which involves a regularization term as well as computation of the Jacobian. A gradient-free Nelder-Mead simplex method is proposed here to perform the image reconstruction procedure and is shown to provide solutions that closely match ones obtained using established methods, even in highly noisy data. The proposed method also has the distinct advantage of being more efficient owing to being regularization free, involving only repeated forward calculations. (C) 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)

DSA image registration using non-uniform MRF model and pivotal control points

In order to reduce the motion artifacts in DSA, non-rigid image registration is commonly used before subtracting the mask from the contrast image. Since DSA registration requires a set of spatially non-uniform control points, a conventional MRF model is not very efficient. In this paper, we introduce the concept of pivotal and non-pivotal control points to address this, and propose a non-uniform MRF for DSA registration. We use quad-trees in a novel way to generate the non-uniform grid of control points. Our MRF formulation produces a smooth displacement field and therefore results in better artifact reduction than that of registering the control points independently. We achieve improved computational performance using pivotal control points without compromising on the artifact reduction. We have tested our approach using several clinical data sets, and have presented the results of quantitative analysis, clinical assessment and performance improvement on a GPU. (C) 2013 Elsevier Ltd. All rights reserved.

Taylor series expansion based multidimensional image reconstruction for confocal and 4pi microscopy

We propose and experimentally demonstrate a three-dimensional (3D) image reconstruction methodology based on Taylor series approximation (TSA) in a Bayesian image reconstruction formulation. TSA incorporates the requirement of analyticity in the image domain, and acts as a finite impulse response filter. This technique is validated on images obtained from widefield, confocal laser scanning fluorescence microscopy and two-photon excited 4pi (2PE-4pi) fluorescence microscopy. Studies on simulated 3D objects, mitochondria-tagged yeast cells (labeled with Mitotracker Orange) and mitochondrial networks (tagged with Green fluorescent protein) show a signal-to-background improvement of 40% and resolution enhancement from 360 to 240 nm. This technique can easily be extended to other imaging modalities (single plane illumination microscopy (SPIM), individual molecule localization SPIM, stimulated emission depletion microscopy and its variants).

Classification, representation, and automatic extraction of deformation features in sheet metal parts

This paper presents classification, representation and extraction of deformation features in sheet-metal parts. The thickness is constant for these shape features and hence these are also referred to as constant thickness features. The deformation feature is represented as a set of faces with a characteristic arrangement among the faces. Deformation of the base-sheet or forming of material creates Bends and Walls with respect to a base-sheet or a reference plane. These are referred to as Basic Deformation Features (BDFs). Compound deformation features having two or more BDFs are defined as characteristic combinations of Bends and Walls and represented as a graph called Basic Deformation Features Graph (BDFG). The graph, therefore, represents a compound deformation feature uniquely. The characteristic arrangement of the faces and type of bends belonging to the feature decide the type and nature of the deformation feature. Algorithms have been developed to extract and identify deformation features from a CAD model of sheet-metal parts. The proposed algorithm does not require folding and unfolding of the part as intermediate steps to recognize deformation features. Representations of typical features are illustrated and results of extracting these deformation features from typical sheet metal parts are presented and discussed. (C) 2013 Elsevier Ltd. All rights reserved.

Classification of Myopathies on Molecular basis in Drosophila using Raman spectroscopy

Myopathies are muscular diseases in which muscle fibers degenerate due to many factors such as nutrient deficiency, infection and mutations in myofibrillar etc. The objective of this study is to identify the bio-markers to distinguish various muscle mutants in Drosophila (fruit fly) using Raman Spectroscopy. Principal Components based Linear Discriminant Analysis (PC-LDA) classification model yielding >95% accuracy was developed to classify such different mutants representing various myopathies according to their physiopathology.

Classification calibration dimension for general multiclass losses

We study consistency properties of surrogate loss functions for general multiclass classification problems, defined by a general loss matrix. We extend the notion of classification calibration, which has been studied for binary and multiclass 0-1 classification problems (and for certain other specific learning problems), to the general multiclass setting, and derive necessary and sufficient conditions for a surrogate loss to be classification calibrated with respect to a loss matrix in this setting. We then introduce the notion of \emph{classification calibration dimension} of a multiclass loss matrix, which measures the smallest `size' of a prediction space for which it is possible to design a convex surrogate that is classification calibrated with respect to the loss matrix. We derive both upper and lower bounds on this quantity, and use these results to analyze various loss matrices. In particular, as one application, we provide a different route from the recent result of Duchi et al.\ (2010) for analyzing the difficulty of designing `low-dimensional' convex surrogates that are consistent with respect to pairwise subset ranking losses. We anticipate the classification calibration dimension may prove to be a useful tool in the study and design of surrogate losses for general multiclass learning problems.

Differentially private stochastic gradient descent algorithm for multiparty classification

We consider the problem of developing privacy-preserving machine learning algorithms in a dis-tributed multiparty setting. Here different parties own different parts of a data set, and the goal is to learn a classifier from the entire data set with-out any party revealing any information about the individual data points it owns. Pathak et al [7]recently proposed a solution to this problem in which each party learns a local classifier from its own data, and a third party then aggregates these classifiers in a privacy-preserving manner using a cryptographic scheme. The generaliza-tion performance of their algorithm is sensitive to the number of parties and the relative frac-tions of data owned by the different parties. In this paper, we describe a new differentially pri-vate algorithm for the multiparty setting that uses a stochastic gradient descent based procedure to directly optimize the overall multiparty ob-jective rather than combining classifiers learned from optimizing local objectives. The algorithm achieves a slightly weaker form of differential privacy than that of [7], but provides improved generalization guarantees that do not depend on the number of parties or the relative sizes of the individual data sets. Experimental results corrob-orate our theoretical findings.

Extension of TSVM to multi-class and hierarchical text classification problems With general losses

Transductive SVM (TSVM) is a well known semi-supervised large margin learning method for binary text classification. In this paper we extend this method to multi-class and hierarchical classification problems. We point out that the determination of labels of unlabeled examples with fixed classifier weights is a linear programming problem. We devise an efficient technique for solving it. The method is applicable to general loss functions. We demonstrate the value of the new method using large margin loss on a number of multi-class and hierarchical classification datasets. For maxent loss we show empirically that our method is better than expectation regularization/constraint and posterior regularization methods, and competitive with the version of entropy regularization method which uses label constraints.

Image reconstruction enables high resolution imaging at large penetration depths in fluorescence microscopy

Imaging thick specimen at a large penetration depth is a challenge in biophysics and material science. Refractive index mismatch results in spherical aberration that is responsible for streaking artifacts, while Poissonian nature of photon emission and scattering introduces noise in the acquired three-dimensional image. To overcome these unwanted artifacts, we introduced a two-fold approach: first, point-spread function modeling with correction for spherical aberration and second, employing maximum-likelihood reconstruction technique to eliminate noise. Experimental results on fluorescent nano-beads and fluorescently coated yeast cells (encaged in Agarose gel) shows substantial minimization of artifacts. The noise is substantially suppressed, whereas the side-lobes (generated by streaking effect) drops by 48.6% as compared to raw data at a depth of 150 mu m. Proposed imaging technique can be integrated to sophisticated fluorescence imaging techniques for rendering high resolution beyond 150 mu m mark. (C) 2013 AIP Publishing LLC.

Experimental analysis of composite single-lap joints using digital image correlation and comparison with theoretical models

The demand for energy efficient, low weight structures has boosted the use of composite structures assembled using increased quantities of structural adhesives. Bonded structures may be subjected to severe working environments such as high temperature and moisture due to which the adhesive gets degraded over a period of time. This reduces the strength of a joint and leads to premature failure. Measurement of strains in the adhesive bondline at any point of time during service may be beneficial as an assessment can be made on the integrity of a joint and necessary preventive actions may be taken before failure. This paper presents an experimental approach of measuring peel and shear strains in the adhesive bondline of composite single-lap joints using digital image correlation. Different sets of composite adhesive joints with varied bond quality were prepared and subjected to tensile load during which digital images were taken and processed using digital image correlation software. The measured peel strain at the joint edge showed a rapid increase with the initiation of a crack till failure of the joint. The measured strains were used to compute the corresponding stresses assuming a plane strain condition and the results were compared with stresses predicted using theoretical models, namely linear and nonlinear adhesive beam models. A similar trend in stress distribution was observed. Further comparison of peel and shear strains also exhibited similar trend for both healthy and degraded joints. Maximum peel stress failure criterion was used to predict the failure load of a composite adhesive joint and a comparison was made between predicted and actual failure loads. The predicted failure loads from theoretical models were found to be higher than the actual failure load for all the joints.