Imran Shafiq Ahmad

A novel SVM+NDA model for classification with an application to face recognition

Support vector machine (SVM) is a powerful classification methodology, where the support vectors fully describe the decision surface by incorporating local information. On the other hand, nonparametric discriminant analysis (NDA) is an improvement over LDA where the normality assumption is relaxed. NDA also detects the dominant normal directions to the decision plane. This paper introduces a novel SVM+NDA model which can be viewed as an extension to the SVM by incorporating some partially global information, especially, discriminatory information in the normal direction to the decision boundary. This can also be considered as an extension to the NDA where the support vectors improve the choice of k-nearest neighbors on the decision boundary by incorporating local information. Being an extension to both SVM and NDA, it can deal with heteroscedastic and non-normal data. It also avoids the small sample size problem. Moreover, it can be reduced to the classical SVM model, so that existing softwares can be used. A kernel extension of the model, called KSVM+KNDA is also proposed to deal with nonlinear problems. We have carried an extensive comparison of the SVM+NDA to the LDA, SVM, heteroscedastic LDA (HLDA), NDA and the combined SVM and LDA on artificial, real and face recognition data sets. Results for KSVM+KNDA have also been presented. These comparisons demonstrate the advantages and superiority of our proposed model.

On-Line Signature Verification by Exploiting Inter-Feature Dependencies

The traditional on-line signature verification process involves use of various dynamic features such as velocity, pressure, acceleration, angles, etc. The idea is to device a composite vector structure combining more than one feature where each feature is treated independently. Our proposed research work is an attempt to exploit the inter-feature dependencies by employing a higher dimensional vector approach. The strategy adopted here is to obtain pressure strokes with respect to various velocity bands. The strokes thus obtained are found to portray a reasonably accurate basis for discriminating genuine vs forgery class. The simulation results validate our assumptions and show improvements in the discriminating index

Indexing and retrieval of images by spatial constraints

Abstract Many multimedia applications require retrieval of spatially similar images against a given query image. Existing work on image retrieval and indexing either requires extensive low-level computations or elaborate human interaction. In this paper, we introduce a new symbolic image representation technique to eliminate repetitive tasks of image understanding and object processing. Our symbolic image representation scheme is based on the concept of hierarchical decomposition of image space into spatial arrangements of features while preserving the spatial relationships among the image objects. Quadtrees are used to manage the decomposition hierarchy and play an important role in defining the similarity measure. This scheme is incremental in nature, can be adopted to accommodate varying levels of details in a wide range of application domains, and provides geometric variance independence. While ensuring that there are no false negatives, our approach also discriminates against non-matching entities by eliminating them as soon as possible, during the coarser matching phases. A hierarchical indexing scheme based on the concept of image signatures and efficient quadtree matching has been devised. Each level of the hierarchy tends to reduce the search space, allowing more involved comparisons only for potentially matching candidate database images. For a given query image, a facility is provided to rank-order the retrieved spatially similar images from the image database for subsequent browsing and selection by the user.

Spatial similarity-based retrievals and image indexing by hierarchical decomposition

For efficient search and spatial similarity based retrieval of image contents, the paper introduces a new symbolic image representation and indexing technique. In this technique, an image is recursively decomposed into a spatial arrangement of feature points while preserving the spatial relationships among its various component. Quadtrees are used to manage the decomposition hierarchy and help in quantifying the measure of similarity. This scheme is incremental in nature and can be adopted to find a match at various levels of details, from coarse to fine. This approach is translation, rotation and scale independent. For search and retrieval, a two phase indexing scheme based on image signatures and quadtree matching is introduced. For a given query image, a facility is provided to rank order the retrieved spatially similar images from the image database against a given query image for subsequent browsing and user selection.

Covariance-guided One-Class Support Vector Machine

In one-class classification, the low variance directions in the training data carry crucial information to build a good model of the target class. Boundary-based methods like One-Class Support Vector Machine (OSVM) preferentially separates the data from outliers along the large variance directions. On the other hand, retaining only the low variance directions can result in sacrificing some initial properties of the original data and is not desirable, specially in case of limited training samples. This paper introduces a Covariance-guided One-Class Support Vector Machine (COSVM) classification method which emphasizes the low variance projectional directions of the training data without compromising any important characteristics. COSVM improves upon the OSVM method by controlling the direction of the separating hyperplane through incorporation of the estimated covariance matrix from the training data. Our proposed method is a convex optimization problem resulting in one global optimum solution which can be solved efficiently with the help of existing numerical methods. The method also keeps the principal structure of the OSVM method intact, and can be implemented easily with the existing OSVM libraries. Comparative experimental results with contemporary one-class classifiers on numerous artificial and benchmark datasets demonstrate that our method results in significantly better classification performance. HighlightsThe low-variance directions are crucial for one-class classification (OCC).A new method of OCC emphasizing the low-variance directions is proposed.The method incorporates covariance information into convex optimization problem.Can be implemented and solved efficiently with existing software.Comparative experiments with contemporary classifiers show positive results.

A Generalized Neural Network Approach to Mobile Robot Navigation and Obstacle Avoidance

Navigation is one of the most important problems in developing and designing intelligent mobile robots. To locally navigate and autonomously plan a path to arrive to a desired destination, Artificial Neural Networks (ANNs) are employed to model complex relationships between inputs and outputs or to find patterns in data as they provide more suitable solutions than the traditional methods. However, current neural network navigation approaches are limited to one kind of robot platform and range sensor, and usually are not extendable to other types of robots with different range sensors without the need to change the network structures. In this paper, we propose a general method to interpret the data from various types of 2-dimensional range sensors and a neural network algorithm to perform the navigation task. Our approach can yield a global navigation algorithm which can be applied to various types of range sensors and robot platforms. Moreover, this method contributes positively to reducing the time required for training the networks.

SN-SVM: a sparse nonparametric support vector machine classifier

This paper introduces a novel sparse nonparametric support vector machine classifier (SN-SVM) which combines data distribution information from two state-of-the-art kernel-based classifiers, namely, the kernel support vector machine (KSVM) and the kernel nonparametric discriminant (KND). The proposed model incorporates some near-global variations of the data provided by the KND and, hence, may be viewed as an extension to the KSVM. Similarly, since the support vectors improve the choice of

Text-based Image Indexing and Retrieval using Formal Concept Analysis

\kappa