Feature subset selection and ranking for data dimensionality reduction

A new unsupervised forward orthogonal search (FOS) algorithm is introduced for feature selection and ranking. In the new algorithm, features are selected in a stepwise way, one at a time, by estimating the capability of each specified candidate feature subset to represent the overall features in the measurement space. A squared correlation function is employed as the criterion to measure the dependency between features and this makes the new algorithm easy to implement. The forward orthogonalization strategy, which combines good effectiveness with high efficiency, enables the new algorithm to produce efficient feature subsets with a clear physical interpretation

Feature subset selection and ranking for data dimensionality reduction

A new unsupervised forward orthogonal search (FOS) algorithm is introduced for feature selection and ranking. In the new algorithm, features are selected in a stepwise way, one at a time, by estimating the capability of each specified candidate feature subset to represent the overall features in the measurement space. A squared correlation function is employed as the criterion to measure the dependency between features and this makes the new algorithm easy to implement. The forward orthogonalization strategy, which combines good effectiveness with high efficiency, enables the new algorithm to produce efficient feature subsets with a clear physical interpretation

A video synchronization approach for coherent key-frame extraction and object segmentation

© 2005 - 2014 JATIT & LLS. All rights reserved. In this paper we discuss a new video frame synchronization approach for coherent key-frame extraction and object segmentation. As two basic units for content-based video analysis, key-frame extraction and object segmentation are usually implemented independently and separately based on different feature sets. Our previous work showed that by exploiting the inherent relationship between key-frames and objects, a set of salient key-frames can be extracted to support robust and efficient object segmentation. This work furthers the previous numerical studies by suggesting a new analytical approach to jointly formulate key-frame extraction and object segmentation via a statistical mixture model where the concept of frame/pixel saliency which is introduced and also this deals with the relationship between the frames. A modified Expectation Maximization algorithm is developed for model estimation that leads to the most salient key-frames for object segmentation. Simulations on both synthetic and real videos show the effectiveness and efficiency of the proposed method

The effect of noise and sample size on an unsupervised feature selection method for manifold learning

The research on unsupervised feature selection is scarce in comparison to that for supervised models, despite the fact that this is an important issue for many clustering problems. An unsupervised feature selection method for general Finite Mixture Models was recently proposed and subsequently extended to Generative Topographic Mapping (GTM), a manifold learning constrained mixture model that provides data visualization. Some of the results of a previous partial assessment of this unsupervised feature selection method for GTM suggested that its performance may be affected by insufficient sample size and by noisy data. In this brief study, we test in some detail such limitations of the method.Postprint (published version

A Multiscale Approach for Statistical Characterization of Functional Images

Increasingly, scientific studies yield functional image data, in which the observed data consist of sets of curves recorded on the pixels of the image. Examples include temporal brain response intensities measured by fMRI and NMR frequency spectra measured at each pixel. This article presents a new methodology for improving the characterization of pixels in functional imaging, formulated as a spatial curve clustering problem. Our method operates on curves as a unit. It is nonparametric and involves multiple stages: (i) wavelet thresholding, aggregation, and Neyman truncation to effectively reduce dimensionality; (ii) clustering based on an extended EM algorithm; and (iii) multiscale penalized dyadic partitioning to create a spatial segmentation. We motivate the different stages with theoretical considerations and arguments, and illustrate the overall procedure on simulated and real datasets. Our method appears to offer substantial improvements over monoscale pixel-wise methods. An Appendix which gives some theoretical justifications of the methodology, computer code, documentation and dataset are available in the online supplements

An Adaptive Vision-based Sensor for Underwater Line Detection Employing Shape and Color Image Segmentation

This work aims the design and implementation of an adaptive vision-based sensor for detecting a pipe on underwater scenes in real time. The motivation is focused to future applications of vision servo control in underwater vehicles. The approach employs color and shape image segmentation together with an adjust mechanism that aims continuously in time to reach the best setup of a parameter set of the color image segmentation. The sensor performs very well even in the case of large and rapid changes in the scene illumination. On the basis of many experiments carried out in real scenes and the comparison with similar algorithms in the state-of-the-art field on the same application, the approach gets a better positioning with respect to related results above all in the case of extremely changing and poor luminance conditions. As drawback, the required computation time to achieve optimal values for the first time (auto-tuning phase) may be large; contrary to the adaptive ongoing process, in where the optimization is much more agile.Fil: Jordan, Mario Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto Argentino de Oceanografía (i); Argentina. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; ArgentinaFil: Trabes, Emanuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto Argentino de Oceanografía (i); Argentin