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European Journal of Scientific Research ISSN 1450-216X Vol.14 No.3 (2006), pp. 426-433 © EuroJournals Publishing, Inc. 2006 http://www.eurojournals.com/ejsr.htm Feature Selection Based on Statistical Analysis Nooritawati Md Tahir Dept. of Electrical, Electronics and Systems Faculty of Engineering Universiti Kebangsaan Malaysia 43600 Bangi, Selangor DE Email: norita@vlsi.eng.ukm.my, norita_tahir@yahoo.com Tel:603-89216035; Fax:603-89216146 Aini Hussain Dept. of Electrical, Electronics and Systems Faculty of Engineering Universiti Kebangsaan Malaysia 43600 Bangi, Selangor DE Tel:603-89216035; Fax:603-89216146 Salina Abdul Samad Dept. of Electrical, Electronics and Systems Faculty of Engineering Universiti Kebangsaan Malaysia 43600 Bangi, Selangor DE Tel:603-89216035; Fax:603-89216146 Hafizah Husain Dept. of Electrical, Electronics and Systems Faculty of Engineering Universiti Kebangsaan Malaysia 43600 Bangi, Selangor DE Tel:603-89216035; Fax:603-89216146 Mohd Yusof Jamaluddin Dept. of Electrical, Electronics and Systems Faculty of Engineering Universiti Kebangsaan Malaysia 43600 Bangi, Selangor DE Tel:603-89216035; Fax:603-89216146 Abstract In most pattern recognition (PR) system, selecting the best feature vectors is an important task. Feature vectors serve as a reduced representation of the original data that facilitate us to evade the curse of dimensionality in a PR task. In this work, we deem further endeavor in selecting the best feature vectors for the PR task that is to determine the best eigenfeatures of four main human postures based on the rules of thumb of Principal Component Analysis namely the KG-rule, Cumulative Variance and the Scree Test. Accordingly, all three rules of thumb suggest in retaining only 9% of the total eigenvectors or also known as ‘eigenpostures’. Next, these eigenpostures are statistically analyzed prior to classification. Thus, the most relevant component of the selected eigenpostures can be ascertained. The statistical significance of the eigenpostures is determined using ANOVA. Further, a Multiple Comparison Procedure (MCP) and homogeneous subsets tests are performed to determine the number of optimized eigenpostures for classification. These optimized eigenpostures will feat as inputs to the Artificial Neural Network (ANN) classifier. The statistical analysis has enabled us to perform effectively the selection of eigenpostures for classification of human postures.
Feature Selection Based on Statistical Analysis 427 Keywords: Statistical Analysis, ANOVA, Principal Component Analysis, Artificial Neural Network. Introduction Pattern recognitions (PR) are defined as a perception task that perceives patterns in terms of characteristic attributes, or features, and classifies these features into distinct classes [1]. A fundamental pattern recognition system includes two principal processes: feature extraction and classification. As we know, the purpose of feature extraction is to characterize attributes pattern belonging to a class. If a complete set of discriminatory features for each pattern class can be found, classification can be reduced to a simple matching process or a table look-up scheme. However, this assumption is really too quixotic to be achieved in practical pattern recognition problems. Therefore, only some, or the best discriminatory features are usually adopted. As for classification, its aim is similar to that of feature extraction, which is to find the best class that is the closest to the classified pattern. One of the issues that necessitate careful thought in a PR system is feature extraction and selection. Feature selection entails the task to select a subset amongst a set of candidate features that performs best under a classification system. This procedure can lessen not only the cost of recognition by reducing the number of features that need to be collected, but in some cases it can also afford better classification accuracy [2]. This is the typical PR framework. Conversely, the Principal Component Analysis (PCA) is an eminent technique that has found great attentions in fields such as face recognition [13], face pose classification [14], facial expressions [15], gesture recognition [16] and detection of human activities [17]. However, the actual number of principal components or eigenvalues to be retained in past work is vague. In this study, we deem further effort in selecting the best feature vectors for the PR task based on the rules of thumb of PCA and performed Statistical Analysis prior to classifications. In doing so, the most relevant component of the selected eigenvectors for classification can be revealed. The structure of this paper includes materials and method in next section followed by results and finally we conclude our findings. Materials and Methods System Overview Figure 1 depicts an overview of the overall PR system that outlines the basic structure. To illustrate the significance of our additional effort, we implement a PR task that handles a large database of human posture images namely standing, sitting, bending and lying as illustrates in Figure 2. The preprocessing stage consists of segmentation, feature extraction and feature selection. This phase extorts the silhouette of a person which incorporate background subtraction followed by thresholding. Median filtering and morphological operations are utilized for noise removal.
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