Orthogonal Features Research Articles

Fusing iris and periocular recognition using discrete orthogonal moment-based invariant feature-set

Iris recognition in uncontrolled environment poses a challenge due to occlusion noise, specular reflections and poor resolution. Therefore, periocular recognition has become a popular biometric modality which when used with iris recognition makes the system suitable for high security applications. The paper introduces discrete orthogonal moment-based invariant features: Tchebichef, Krawtchouk and Dual-Hahn moments which provide discriminative features with compact information and minimum redundancy for non-ideal conditions. The proposed techniques are applied on two publicly available iris databases: IITD v1 and UBIRIS v2 and our own PEC, Chandigarh periocular database. Results demonstrate that the moment-based feature-set outperforms existing approaches available in the literature.

Robust Iris Recognition Using Moment Invariants

Iris recognition under less constrained environment poses a challenge to be considered for high-security applications. In this paper, discrete orthogonal moment-based features including Tchebichef, Krawtchouk and Dual-Hahn are proposed which prove to be effective for both near-infrared and visible images. The local as well as global features are extracted from localized iris regions till 15th order with invariance (scale, rotation, translation and illumination) properties and tolerance to noise. The performance of the moment-based features is evaluated on four publicly available databases: CASIA-IrisV4-Interval, IITD.v1, UPOL and UBIRIS.v2. It is found that the proposed method gives encouraging results in terms of accuracy, equal error rate and decidability index as compared to the competing techniques available in the literature.

Fast Non-Negative Matrix Factorizations for Face Recognition

Non-negative Matrix Factorization (NMF), as a promising image-data representation approach, encounters the problems of slow convergence and weak classification ability. To overcome these limitations, this paper, based on different error measurements, proposes two kinds of NMF algorithms with fast gradient descent and high discriminant performance. It is shown that the proposed Fast NMF (FNMF) methods have larger step sizes than those of traditional NMFs. Moreover, the traditional NMFs are the special cases of our methods. To further enhance the discriminative power of non-negative features, we exploit our previous block NMF technique and obtain Block FNMF (BFNMF) algorithms, which are supervised decomposition approaches with some good properties, such as the highly sparse features and orthogonal features from different classes. In experiments, both convergence on non-negative decomposition and performance on face recognition (FR) are considered for evaluations. Compared with traditional NMF algorithms and some state-of-the-art methods, experimental results indicate the effective and superior performance of the proposed NMF methods.

On the expressive power of user-defined effects: effect handlers, monadic reflection, delimited control

We compare the expressive power of three programming abstractions for user-defined computational effects: Plotkin and Pretnar's effect handlers, Filinski's monadic reflection, and delimited control without answer-type-modification. This comparison allows a precise discussion about the relative expressiveness of each programming abstraction. It also demonstrates the sensitivity of the relative expressiveness of user-defined effects to seemingly orthogonal language features. We present three calculi, one per abstraction, extending Levy's call-by-push-value. For each calculus, we present syntax, operational semantics, a natural type-and-effect system, and, for effect handlers and monadic reflection, a set-theoretic denotational semantics. We establish their basic metatheoretic properties: safety, termination, and, where applicable, soundness and adequacy. Using Felleisen's notion of a macro translation, we show that these abstractions can macro-express each other, and show which translations preserve typeability. We use the adequate finitary set-theoretic denotational semantics for the monadic calculus to show that effect handlers cannot be macro-expressed while preserving typeability either by monadic reflection or by delimited control. Our argument fails with simple changes to the type system such as polymorphism and inductive types. We supplement our development with a mechanised Abella formalisation.

A filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization for biomedical data mining

A filter feature selection technique has been widely used to mine biomedical data. Recently, in the classical filter method minimal-Redundancy-Maximal-Relevance (mRMR), a risk has been revealed that a specific part of the redundancy, called irrelevant redundancy, may be involved in the minimal-redundancy component of this method. Thus, a few attempts to eliminate the irrelevant redundancy by attaching additional procedures to mRMR, such as Kernel Canonical Correlation Analysis based mRMR (KCCAmRMR), have been made. In the present study, a novel filter feature selection method based on the Maximal Information Coefficient (MIC) and Gram-Schmidt Orthogonalization (GSO), named Orthogonal MIC Feature Selection (OMICFS), was proposed to solve this problem. Different from other improved approaches under the max-relevance and min-redundancy criterion, in the proposed method, the MIC is used to quantify the degree of relevance between feature variables and target variable, the GSO is devoted to calculating the orthogonalized variable of a candidate feature with respect to previously selected features, and the max-relevance and min-redundancy can be indirectly optimized by maximizing the MIC relevance between the GSO orthogonalized variable and target. This orthogonalization strategy allows OMICFS to exclude the irrelevant redundancy without any additional procedures. To verify the performance, OMICFS was compared with other filter feature selection methods in terms of both classification accuracy and computational efficiency by conducting classification experiments on two types of biomedical datasets. The results showed that OMICFS outperforms the other methods in most cases. In addition, differences between these methods were analyzed, and the application of OMICFS in the mining of high-dimensional biomedical data was discussed. The Matlab code for the proposed method is available at https://github.com/lhqxinghun/bioinformatics/tree/master/OMICFS/.

A systematic identification of species-specific protein succinylation sites using joint element features information.

Lysine succinylation, an important type of protein posttranslational modification, plays significant roles in many cellular processes. Accurate identification of succinylation sites can facilitate our understanding about the molecular mechanism and potential roles of lysine succinylation. However, even in well-studied systems, a majority of the succinylation sites remain undetected because the traditional experimental approaches to succinylation site identification are often costly, time-consuming, and laborious. In silico approach, on the other hand, is potentially an alternative strategy to predict succinylation substrates. In this paper, a novel computational predictor SuccinSite2.0 was developed for predicting generic and species-specific protein succinylation sites. This predictor takes the composition of profile-based amino acid and orthogonal binary features, which were used to train a random forest classifier. We demonstrated that the proposed SuccinSite2.0 predictor outperformed other currently existing implementations on a complementarily independent dataset. Furthermore, the important features that make visible contributions to species-specific and cross-species-specific prediction of protein succinylation site were analyzed. The proposed predictor is anticipated to be a useful computational resource for lysine succinylation site prediction. The integrated species-specific online tool of SuccinSite2.0 is publicly accessible.

Quaternion Locality-Constrained Coding for Color Face Hallucination.

Recently, the locality linear coding (LLC) has attracted more and more attentions in the areas of image processing and computer vision. However, the conventional LLC with real setting is just designed for the grayscale image. For the color image, it usually treats each color channel individually or encodes the monochrome image by concatenating all the color channels, which ignores the correlations among different channels. In this paper, we propose a quaternion-based locality-constrained coding (QLC) model for color face hallucination in the quaternion space. In QLC, the face images are represented as quaternion matrices. By transforming the channel images into an orthogonal feature space and encoding the coefficients in the quaternion domain, the proposed QLC is expected to learn the advantages of both quaternion algebra and locality coding scheme. Hence, the QLC cannot only expose the true topology of image patch manifold but also preserve the inherent correlations among different color channels. Experimental results demonstrated that our proposed QLC method achieved superior performance in color face hallucination compared with other state-of-the-art methods.

Identification of ISL Alphabets Using Discrete Orthogonal Moments

In this paper, discrete orthogonal moment-based shape features up to 5th order are proposed for Indian sign language (ISL) recognition system. The shape recognition capability of discrete orthogonal moment-based local features is verified on two databases. These include the standard Jochen-Triesch’s database and 26 ISL alphabets. The ISL alphabets are collected on both uniform and complex backgrounds, with variations in position, scale and rotation. The feature-set is increased for 26 ISL alphabets by varying Region of Interest (ROI) and extracting features from each ROI. A minimum possible feature-set with least redundancy is selected that gives the best recognition accuracy. The effect of order and feature dimensionality for different classifiers is studied. Results show that both Dual-Hahn and Krawtchouk moments are found to exhibit user, scale, rotation and translation invariance. Moreover, they have shape identification capability, thus achieving good recognition accuracy.

The Hybrid Filter Feature Selection Methods for Improving High-Dimensional Text Categorization

The bag-of-words technique is often used to present a document in text categorization. However, for a large set of documents where the dimension of the bag-of-words vector is very high, text categorization becomes a serious challenge as a result of sparse data, over-fitting, and irrelevant features. A filter feature selection method reduces the number of features by eliminating irrelevant features from the bag-of-words vector. In this paper, we analyze the weak points and strong points of two filter feature selection approaches which are the frequency-based approach and the cluster-based approach. Thanks to the analysis, we propose hybrid filter feature selection methods, named the Frequency-Cluster Feature Selection (FCFS) and the Detailed Frequency-Cluster Feature Selection (DtFCFS), to further improve the performance of the filter feature selection process in text categorization. The FCFS is a combination of the Frequency-based approach and the Cluster-based approach, while the DtFCFS, a detailed version of the FCFS, is a comprehensively hybrid clusterbased method. We do experiments with four benchmark datasets (the Reuters-21578 and Newsgroup dataset for news classification, the Ohsumed dataset for medical document classification, and the LingSpam dataset for email classification) to compare the proposed methods with six related wellknown methods such as the Comprehensive Measurement Feature Selection (CMFS), the Optimal Orthogonal Centroid Feature Selection (OCFS), the Crossed Centroid Feature Selection (CIIC), the Information Gain (IG), the Chi-square (CHI), and the Deviation from Poisson Feature Selection (DFPFS). In terms of the Micro-F1, the Macro-F1, and the dimension reduction rate, the DtFCFS is superior to the other methods, while the FCFS shows competitive and even superior performance to the good methods, especially for the Macro-F1.

Tracking Social Groups Within and Across Cameras

We propose a method for tracking groups from single and multiple cameras with disjointed fields of view. Our formulation follows the tracking-by-detection paradigm in which groups are the atomic entities and are linked over time to form long and consistent trajectories. To this end, we formulate the problem as a supervised clustering problem in which a structural SVM classifier learns a similarity measure appropriate for group entities. Multicamera group tracking is handled inside the framework by adopting an orthogonal feature encoding that allows the classifier to learn inter- and intra-camera feature weights differently. Experiments were carried out on a novel annotated group tracking data set, the DukeMTMC-Groups data set. Since this is the first data set on the problem, it comes with the proposal of a suitable evaluation measure. Results of adopting learning for the task are encouraging, scoring a +15% improvement in F1 measure over a nonlearning-based clustering baseline. To the best of our knowledge, this is the first proposal of its kind dealing with multicamera group tracking.

Cast-in-place, ambiently-dried, silica-based, high-temperature insulation

A novel sol-gel chemistry approach was developed to enable the simple integration of a cast-in-place, ambiently-dried insulation into high temperature applications. The insulation was silica-based and synthesized using methyltrimethoxysilane (MTMS) as the precursor. MTMS created a unique silica microstructure that was mechanically robust, macroporous, and superhydrophobic. To allow for casting into and around small, orthogonal features, zirconia fibers were added to increase stiffness and minimize contraction that could otherwise cause cracking during drying. Nano-sized titania powder was incorporated as an opacifier to reduce radiative heat transport. To assess relevance to high temperature thermoelectric generator technology, a comprehensive set of materials characterization experiments were conducted. The silica gel was thermally stable, retained superhydrophobicity with a water contact angle >150°, and showed a high electrical resistance >1 GΩ, regardless of heating temperature (up to 600 °C in Ar for 4 h). In addition, The silica-based thermal insulation exhibited a Young's modulus ∼3.7 MPa and a low thermal conductivity <0.08 W/(m.K) at room temperature before and after heat treatment (up to 600 °C in Ar for 4 h). Thus, based on the simplicity of the manufacturing process and the optimized material properties, we believe this technology can act as an effective cast-in-place thermal insulation (CTI) for thermoelectric generators and myriad other applications requiring improved thermal efficiency.

Practical Homomorphic Message Authenticators for Arithmetic Circuits

Homomorphic message authenticators allow the holder of a (public) evaluation key to perform computations over previously authenticated data, in such a way that the produced tag $$\sigma $$ź can be used to certify the authenticity of the computation. More precisely, a user, knowing the secret key $$\mathsf{sk}$$sk used to authenticate the original data, can verify that $$\sigma $$ź authenticates the correct output of the computation. This primitive has been recently formalized by Gennaro and Wichs, who also showed how to realize it from fully homomorphic encryption. In this paper, we show new constructions of this primitive that, while supporting a smaller set of functionalities (i.e., polynomially bounded arithmetic circuits as opposite to boolean ones), are much more efficient and easy to implement. Moreover, our schemes can tolerate any number of (malicious) verification queries. Our first construction relies on the sole assumption that one-way functions exist, allows for arbitrary composition (i.e., outputs of previously authenticated computations can be used as inputs for new ones) but has the drawback that the size of the produced tags grows with the degree of the circuit. Our second solution, relying on the D-Diffie-Hellman Inversion assumption, offers somewhat orthogonal features as it allows for very short tags (one single group element!) but poses some restrictions on the composition side.

Analysis of CT lung images using orthogonal moment features

This paper is a study on texture analysis of the CT lung images by the segmentation methods with orthogonal moments features. The CT lung images, normal, cavitary and miliary tuberculosis are taken for study. Owing to the features of orthogonal moments are found suitable for the medical image diagnosis. The segmentation method includes histogram, Watershed and Edge based Segmentation as a base for the evaluation. The orthogonal features used here are Tchebychef, Legendre and Multi resolution enhanced orthogonal polynomial model. In order to get the features, the orthogonal moments are computed from the segmented image. The proposed work is a comparative study on performance in terms of accuracy based on orthogonal moment with segmentation methods. The accuracy is obtained through sensitivity and specificity which are computed from texture features and intensity features. The results reveal that the computation model is an efficacy for medical images.

Analysis of CT lung images using orthogonal moment features

This paper is a study on texture analysis of the CT lung images by the segmentation methods with orthogonal moments features. The CT lung images, normal, cavitary and miliary tuberculosis are taken for study. Owing to the features of orthogonal moments are found suitable for the medical image diagnosis. The segmentation method includes histogram, Watershed and Edge based Segmentation as a base for the evaluation. The orthogonal features used here are Tchebychef, Legendre and Multi resolution enhanced orthogonal polynomial model. In order to get the features, the orthogonal moments are computed from the segmented image. The proposed work is a comparative study on performance in terms of accuracy based on orthogonal moment with segmentation methods. The accuracy is obtained through sensitivity and specificity which are computed from texture features and intensity features. The results reveal that the computation model is an efficacy for medical images.

On Orthogonal Feature Extraction model with applications in medical prognosis

Microarray-based gene expression profiling for cancer prognosis can shed light on providing prognostic information and it has statistically become a highly reliable technique. From a computer scientist’s point of view, cancer prognosis can be termed as a classification problem. In microarray-based cancer prognosis problems, selection of critical attributes is extremely popular as a lot of features are irrelevant or redundant. Appropriate feature selection not only can filter irrelevant features but also improve prediction accuracy. Various models and methods have been developed to rank the importance of features. However, there is no dominant strategy because all methods have their own advantages and drawbacks. In this paper, we propose an Orthogonal Feature Extraction (OFE) model based on feature ranking techniques, which aims at improving cancer prediction accuracy. Our proposed model is compared with the most widely used feature extraction method: Principal Component Analysis (PCA), and two other existing feature extraction methods: Neighborhood Component Analysis (NCA) and Linear Discriminant Analysis (LDA) through 5-fold cross-validation. Numerical results indicated that OFE method can efficiently construct combinations of significant variables enabling computational complexity reduction and also provide recognizable better performance.

FEATURE SELECTION USING SINGULAR VALUE DECOMPOSITION AND ORTHOGONAL CENTROID FEATURE SELECTION FOR TEXT CLASSIFICATION

Text mining is a narrow research field of data mining, which focuses on discovering new information from text document collections, mainly by using techniques from data mining, machine learning, natural language processing and information retrieval. Text classification is the process of analyzing text content and then giving decision whether this text can belong to one group, many groups or it does not belong to the text group which is defined before. On over the world, there have been many effective researches on this problem, especially on texts in English. However, there have been few researches on Vietnamese texts. Moreover, these researching results and applications are still limited partly due to the typical characteristics of Vietnamese language in term of words and sentences and there are many words with many meanings in many different contexts. Text classification problem is the one with many featues, thus to improve the effectiveness of text classification is the aim of may researchers. In this research, the author constructs two methods of feature selection: singular value decomposition and optimal orthogonal centroid feature selection in text classification with high efficiency of calculation proven on English text document and now they are proven on Vietnamese text document. There are many classification techniques, but we implemented on the learning machine algorithms support vector machines. This method has been proven to be effective for text classification problems. With the technique of feature selection singular value decomposition and optimal orthogonal centroid feature selection, the implementing result higher than that of traditional method.

Orthogonal Image Features for Visual Servoing of a 6-DOF Manipulator With Uncalibrated Stereo Cameras

We present an approach to control a 6-degree-of-freedom (DOF) manipulator using an uncalibrated visual servoing (VS) approach that addresses the challenges of choosing proper image features for target objects and designing a VS controller to enhance the tracking performance. The main contribution of this paper is the definition of a new virtual visual space (image space). A novel stereo camera model employing virtual orthogonal cameras is used to map 6-D poses from Cartesian space to this virtual visual space . Each component of the 6-D pose vector defined in this virtual visual space is linearly independent, leading to a full-rank $6\times 6$ image Jacobian matrix , which allows avoiding classical problems, such as image space singularities and local minima. Furthermore, the control for rotational and translational motion of robot is decoupled due to the diagonal image Jacobian. Finally, simulation results with an eye-to-hand robotic system confirm the improvement in controller stability and motion performance with respect to conventional VS approaches. Experimental results on a 6-DOF industrial robot are provided to illustrate the effectiveness of the proposed method and the feasibility of using this method in practical scenarios.

A Novel Feature Selection Based Gravitation for Text Categorization

The high dimensionality of feature space is a big hurdle in applying many sophisticated methods to text categorization. The feature selection method is one of methods which reduce the high dimensionality of feature space. In this paper, we proposed a new feature selection algorithm based on gravitation, named GFS, which regards a feature occurring in one category as an object, and all objects corresponding to a feature occurring in various categories can constitute a gravitational field, then the gravitation of a feature with unknown category label on which all objects in the gravitational field act is used for feature selection. We have evaluated GFS on three benchmark datasets (20-Newgroups, Reuters-21578 and WebKB), using two classification algorithms, Naive Bayes (NB) and Support Vector Machines (SVM), and compared it with four well-known feature selection algorithms (information gain, document frequency, orthogonal centroid feature selection and Poisson distribution). The experiments show that GFS performs significantly better than other feature selection algorithms in terms of micro F1, macro F1 and accuracy.

Abstract P4-02-07: Association between computer-derived features of the ipsilateral breast on DCE-MRI and the 70-gene signature in patients with invasive breast cancer

Abstract Introduction Molecular assays such as the 70-gene signature are increasingly used as prognostic indicators to select chemotherapy in individual patients. These assays are typically derived from postoperative excision specimens and require several weeks to complete. Earlier assessment of the results of such assays could open up new therapeutic options in subgroups of patients, potentially avoiding overtreatment of early breast cancer. Although molecular assays may be derived from biopsied tissue, tumor heterogeneity may cause uncertainty. Dynamic contrast-enhanced MRI (DCE-MRI) depicts some of the hallmarks of cancer that are tested by these molecular essays. The goal of this study was to investigate the association between the postoperatively derived 70-gene signature and computer-derived DCE-MRI features of the ipsilateral breast prior to surgery. Material and Methods Sixty-nine patients with node-negative invasive breast cancer were enrolled between 2003 and 2006. These patients received a preoperative MRI in study setting and a postoperative 70-gene signature assay. Association between preoperative features and the 70-gene signature was evaluated using a computer prediction model combining clinical features and automatically extracted MRI features. The clinical features were age at diagnosis and largest tumor diameter on MRI. The MRI features were rate of contrast uptake in the tumor, rate of wash-out, tumor volume, and two features from the intramammary blood vessel tree (total length and mean rate of contrast uptake). The features were transformed into an orthogonal feature set using principal component analysis. Association with the 70-gene signature (positive or negative indication for systemic therapy) was evaluated using binary logistic regression. Model performance was measured using the area under the receiver operating characteristics curve (AUC) after bootstrap validation using 200 iterations. Two operating points were examined: one to predict a positive 70-gene signature with high certainty (i.e., at high positive-predictive value (PPV)) and one to predict a negative signature with high certainty (i.e., at high negative-predictive value (PPV)). Results The average patient age at diagnosis was 48 years (range: 32-58). The median largest tumor diameter on MRI was 17 mm (range: 5-40). The 70-gene signature was positive in 29/69 (42%) patients. The computer prediction model achieved an AUC of 0.72 after bootstrap validation. At high PPV, 10/29 (34.5%) positive 70-gene signatures were identified preoperatively at the expense of 2/40 (5.0%) false-positive. The PPV was 10/12 (83.3%). At high NPV, 12/40 (30.0%) negative 70-gene signatures were identified at the expense of 1/29 (3.5%) false-negative signature. The NPV was 12/13 (92.3%). Conclusion Computer-derived DCE-MRI features from the ipsilateral breast in combination with clinical parameters show potential to preoperatively assess a negative outcome of the 70-gene signature in approximately one-third of the total patient group. Citation Format: van der Velden BHM, Schmitz AMTh, Loo CE, Gilhuijs KGA. Association between computer-derived features of the ipsilateral breast on DCE-MRI and the 70-gene signature in patients with invasive breast cancer. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P4-02-07.

A Simple Computational Approach for the Texture Analysis of CT Scan Images Using Orthogonal Moments

This paper is a study on texture analysis of Computer Tomography (CT) liver images using orthogonal moment features. Orthogonal moments are used as image feature representation in many applications like invariant pattern recognition of images. Orthogonal moments are proposed here for the diagnosis of any abnormalities on the CT images. The objective of the proposed work is to carry out the comparative study of the performance of orthogonal moments like Zernike, Racah and Legendre moments for the detection of abnormal tissue on CT liver images. The Region of Interest (ROI) based segmentation and watershed segmentation are applied to the input image and the features are extracted with the orthogonal moments and analyses are made with the combination of orthogonal moment with segmentation that provides better accuracy while detecting the tumor. This computational model is tested with many inputs and the performance of the orthogonal moments with segmentation for the texture analysis of CT scan images is computed and compared.