Loh Ai Poh

HAND POSTURE AND FACE RECOGNITION USING A FUZZY-ROUGH APPROACH

A fuzzy-rough multi cluster (FRMC) classifier for the recognition of hand postures and face is presented in this chapter. Features of the image are extracted using the computational model of the ventral stream of visual cortex. The recognition algorithm translates each quantitative value of the feature into fuzzy sets of linguistic terms using membership functions. The membership functions are generated by the fuzzy partitioning of the feature space into fuzzy equivalence classes, using the feature cluster centers generated by the subtractive clustering technique. A rule base generated from the lower and upper approximations of the fuzzy equivalence classes classifies the images through a voting process. Using Genetic Algorithm (GA), the number of features required for classification is reduced by identifying the predictive image features. The margin of classification, which is a measure of the discriminative power of the classifier, is used to ensure the quality of classification process. The fitness function suggested assists in the feature selection process without compromising on the classification accuracy and margin. The algorithm is tested using two hand posture and three face datasets. The algorithm provides good classification accuracy, at a less computational effort. The selection of relevant features further reduced the computational costs of both feature extraction and classification algorithms, which makes it suitable for real-time applications. The performance of the algorithm is compared with that of Support Vector Machines.

Fuzzy-rough discriminative feature selection and classification algorithm, with application to microarray and image datasets

Abstract: A novel algorithm based on fuzzy-rough sets is proposed for the feature selection and classification of datasets with multiple features, with less computational efforts. The algorithm translates each quantitative value of a feature into fuzzy sets of linguistic terms using membership functions and, identifies the discriminative features. The membership functions are formed by partitioning the feature space into fuzzy equivalence classes, using feature cluster centers identified by the subtractive clustering technique. The lower and upper approximations of the fuzzy equivalence classes are obtained and the discriminative features in the dataset are selected. Classification rules are generated using the fuzzy membership values that partition the lower and upper approximations. The classification is done through a voting process. Both the feature selection and classification algorithms have polynomial time complexity. The algorithm is tested in two types of classification problems namely cancer classification and image pattern classification. The large number of gene expression profiles and relatively small number of available samples make the feature selection a key step in microarray based cancer classification. The proposed algorithm identified the relevant features (predictive genes in the case of cancer data) and provided good classification accuracy, at a less computational cost, with good margin of classification. A comparison of the performance of the proposed classifier with relevant classification methods shows its better discriminative power.

Graph matching based hand posture recognition using neuro-biologically inspired features

An elastic graph matching algorithm using biologically inspired features is proposed for the recognition of hand postures. Each node in the graph is labeled using an image feature extracted using the computational model of the ventral stream of visual cortex. The graph nodes are assigned to geometrically significant positions in the hand image, and, the model graphs are created. Bunch graph method is used for modeling the variability in hand posture appearance. Recognition of a hand posture is done by the elastic graph matching between the model graphs and the input image. A radial basis function is used as the similarity function for the matching process. The proposed algorithm is tested on a 10 class hand posture database which consists of 478 grey scale images with light and dark backgrounds. The algorithm provided better recognition accuracy (96.35%) compared to the reported results (93.77%) in the literature.

Identifying social groups in pedestrian crowd videos

A Non-recursive Motion Similarity Clustering (NMSC) algorithm is proposed to identify pedestrians traveling together in social groups. The clustering algorithm is unsupervised and can automatically identify social groups within a region of interest in a video. Social groups are identified using only pedestrian motion information by imposing motion parameter thresholds defined by social psychological principles. Social groups are identified without any prior training. In addition to detecting small social groups, NMSC also detects short-term groups (occurring for a few seconds) and social groups with sparsely distributed pedestrians. The real-time performance and group identification accuracy reveal that the proposed clustering algorithm performs better compared to existing algorithms even for scenes with a large number of pedestrians.

Computational Intelligence Techniques

The chapter explains the computational intelligence techniques utilized in the algorithms presented in the book. The fuzzy and rough sets, fuzzy-rough sets, genetic algorithm and, feature selection and classification using the fuzzy-rough sets are detailed. The biologically inspired feature extraction system utilized in the presented algorithms is explained.

Computational Intelligence in Multi-Feature Visual Pattern Recognition: Hand Posture and Face Recognition using Biologically Inspired Approaches

This book presents a collection of computational intelligence algorithms that addresses issues in visual pattern recognition such as high computational complexity, abundance of pattern features, sensitivity to size and shape variations and poor performance against complex backgrounds. The book has 3 parts. Part 1 describes various research issues in the field with a survey of the related literature. Part 2 presents computational intelligence based algorithms for feature selection and classification. The algorithms are discriminative and fast. The main application area considered is hand posture recognition. The book also discusses utility of these algorithms in other visual as well as non-visual pattern recognition tasks including face recognition, general object recognition and cancer / tumor classification. Part 3 presents biologically inspired algorithms for feature extraction. The visual cortex model based features discussed have invariance with respect to appearance and size of the hand, and provide good inter class discrimination. A Bayesian model of visual attention is described which is effective in handling complex background problem in hand posture recognition. The book provides qualitative and quantitative performance comparisons for the algorithms outlined, with other standard methods in machine learning and computer vision. The book is self-contained with several figures, charts, tables and equations helping the reader to understand the material presented without instruction.

Multi-Feature Pattern Recognition

This chapter focuses on feature selection and classification of multi-feature patterns. Micro array based cancer classification and image based face recognition are discussed. A detailed review of hand gesture recognition algorithms and techniques is included. The hand gesture recognition algorithms are surveyed by classifying them into three categories (a) hidden Markov model based methods, (b) neural network and learning based methods, and (c) the other methods. A list of available hand gesture databases is provided.

Attention Based Segmentation and Recognition Algorithm for Hand Postures Against Complex Backgrounds

The Attention based Segmentation and Recognition (ASR) algorithm for hand postures against complex backgrounds is discussed in this chapter. The ASR algorithm can detect, segment and recognize multi-class hand postures. Visual attention, which is a cognitive process of selectively concentrating on a region of interest in visual field, helps humans to recognize objects in cluttered natural scenes. The ASR algorithm utilizes a Bayesian model of visual attention to generate a saliency map, and to detect and identify the hand region. Feature based visual attention is implemented using a combination of high level (shape, texture) and low level (color) image features. The shape and texture features are extracted from a skin similarity map, using a computational model of the ventral stream of visual cortex. The skin similarity map, which represents the similarity of each pixel to the human skin color in HSI color space, enhances the edges and shapes within the skin colored regions. The color features used are discretized chrominance components in HSI, YCbCr color spaces, and similarity-to-skin map. The hand postures are classified using shape and texture features, with a support vector machines classifier. The NUS hand posture dataset-II with 10 classes of complex background hand postures is utilized for testing the algorithm. The dataset contains hand postures from 40 subjects of different ethnicities. A total of 2,750 hand postures and 2,000 background images are available in the dataset. The hand postures vary in size and shape. The ASR algorithm is tested for hand detection and hand posture recognition using 10 fold cross-validation. The experimental results show that the algorithm has a person independent performance, and is reliable against variations in hand sizes and complex backgrounds.

Fuzzy-Rough Discriminative Feature Selection and Classification

Classification of datasets with multiple features is computationally intensive. Fuzzy-rough set based feature selection and classification requires reduced computational efforts. Lower and upper approximations of fuzzy equivalence classes are useful in finding discriminative features and classification boundaries in a dataset. This chapter discusses a fuzzy-rough single cluster (FRSC) classifier which is a discriminative feature selection and classification algorithm. The FRSC classifier translates each quantitative value of a feature into fuzzy sets of linguistic terms using membership functions and, identifies discriminative features. The membership functions are formed by partitioning the feature space into fuzzy equivalence classes, using feature cluster centers identified through subtractive clustering. Classification rules are generated using fuzzy membership values partitioning the lower and upper approximations. The patterns are classified through a voting process. Both the feature selection and classification algorithms have polynomial time complexity. The algorithm is tested in two types of classification problems, namely, cancer and image-pattern classification. The large number of gene expression profiles and relatively small number of available samples make the feature selection a key step in microarray based cancer classification. The algorithm identified relevant features (predictive genes in the case of cancer data) and provided good classification accuracy, at a less computational cost, with good margin of classification. A comparison of the performance of the FRSC classifier with other relevant classification methods shows the classifier’s better discriminative power.

Visual Pattern Recognition

An overview of the visual pattern recognition process and associated key issues are presented in this chapter. The varying scales and shapes, inter-class similarity, large number of features, and complex backgrounds are issues related to visual pattern recognition. The book focuses on these issues. The chapter introduces different algorithms addressing these issues.