Incremental and Decremental Nonparametric Discriminant Analysis for Face Recognition

Nonparametric Discriminant Analysis (NDA) possesses inherent advantages over Linear Discriminant Analysis (LDA) such as capturing the boundary structure of samples and avoiding matrix inversion. In this paper, we present a novel method for constructing an updated Nonparametric Discriminant Analysis (NDA) model for face recognition. The proposed method is applicable to scenarios where bursts of data samples are added to the existing model in random chunks. Also, the samples which degrade the performance of the model need to be removed. For both of these problems, we propose incremental NDA (INDA) and decremental NDA (DNDA) respectively. Experimental results on four publicly available datasets viz. AR, PIE, ORL and Yale show the efficacy of the proposed method. Also, the proposed method requires less computation time in comparison to batch NDA which makes it suitable for real time applications

Various Approaches of Support vector Machines and combined Classifiers in Face Recognition

In this paper we present the various approaches used in face recognition from 2001-2012.because in last decade face recognition is using in many fields like Security sectors, identity authentication. Today we need correct and speedy performance in face recognition. This time the face recognition technology is in matured stage because research is conducting continuously in this field. Some extensions of Support vector machine (SVM) is reviewed that gives amazing performance in face recognition.Here we also review some papers of combined classifier approaches that is also a dynamic research area in a pattern recognition

Crowd Scene Analysis in Video Surveillance

There is an increasing interest in crowd scene analysis in video surveillance due to the ubiquitously deployed video surveillance systems in public places with high density of objects amid the increasing concern on public security and safety. A comprehensive crowd scene analysis approach is required to not only be able to recognize crowd events and detect abnormal events, but also update the innate learning model in an online, real-time fashion. To this end, a set of approaches for Crowd Event Recognition (CER) and Abnormal Event Detection (AED) are developed in this thesis. To address the problem of curse of dimensionality, we propose a video manifold learning method for crowd event analysis. A novel feature descriptor is proposed to encode regional optical flow features of video frames, where adaptive quantization and binarization of the feature code are employed to improve the discriminant ability of crowd motion patterns. Using the feature code as input, a linear dimensionality reduction algorithm that preserves both the intrinsic spatial and temporal properties is proposed, where the generated low-dimensional video manifolds are conducted for CER and AED. Moreover, we introduce a framework for AED by integrating a novel incremental and decremental One-Class Support Vector Machine (OCSVM) with a sliding buffer. It not only updates the model in an online fashion with low computational cost, but also adapts to concept drift by discarding obsolete patterns. Furthermore, the framework has been improved by introducing Multiple Incremental and Decremental Learning (MIDL), kernel fusion, and multiple target tracking, which leads to more accurate and faster AED. In addition, we develop a framework for another video content analysis task, i.e., shot boundary detection. Specifically, instead of directly assessing the pairwise difference between consecutive frames over time, we propose to evaluate a divergence measure between two OCSVM classifiers trained on two successive frame sets, which is more robust to noise and gradual transitions such as fade-in and fade-out. To speed up the processing procedure, the two OCSVM classifiers are updated online by the MIDL proposed for AED. Extensive experiments on five benchmark datasets validate the effectiveness and efficiency of our approaches in comparison with the state of the art

QUATERNARY STRUCTURE SYNTHESIS IN ALGEBRAIC BAYESIAN NETWORKS: INCREMENTAL AND DECREMENTAL ALGORITHMS

Subject of Research. Algebraic Bayesian networks are referred to a class of probabilistic graphical models that are a representation of knowledge bases with uncertainty. The distinguishing feature of ABN is the availability of global structures. Among them there are primary and secondary structures that are directly used in various kinds of probabilistic logical inference as well as tertiary and quaternary involved in the problems of automatic synthesis and identification of the properties of the secondary structure and partially in the machine learning tasks within specified networks. Existing algorithms for quaternary structure changing require its complete rebuild when changing the primary structure. That feature slows down the whole global structures synthesis, dispels user’s attention who is forced to re-analyze the whole rebuilt structure instead of focusing on the changes that were directly caused by the limited modification of the original data. This fact reduces ABN attractiveness as a model for data processing in general. Scope of Research. This paper is aimed at speeding up the rebuild process and eliminating the shortage of the quaternary structure rebuild algorithms when adding and deleting vertices of primary structure expressed in excess rebuild of the entire structure. The task of algorithm incrementalization for quaternary structure rebuild is solved to achieve the goal. Method. The proposed approach is based on the properties of incremental algorithms that reduce the amount of computations due to the result obtained at the previous step of the algorithm. All the arguments used in the paper are expressed in a graph theory language to apply the established system of terms and classical results. Main Results. The paper presents incremental and decremental algorithms, complemented by a proof of correctness and listing. Given algorithms are based on the previously obtained incremental algorithms for tertiary structure. Moreover, a detailed analysis of the plurality of separators is carried out on each stage of the algorithms. Theoretical and Practical Relevance. These algorithms develop a global structure of algebraic Bayesian networks as well as the theory of probabilistic graphical models in general. Furthermore, they create the groundwork for creation of the secondary structure invariants that may be non-unique even if the primary structure is fixed unlike the current approach where connections are included in the secondary structure. The deletion of manipulation with a set of secondary structures considerably simplifies and makes foreseeable visualization of such complex object as ABN and may improve the computational characteristics of probabilistic logical inference algorithms. It also enables to reformulate the problem of ABN machine learning excluding any need for synthesis of many different objects with complex structure and virtually the same semantics. It also can be expected that the obtained incremental algorithms will accelerate computational processes of rebuilding and properties analysis for all four global ABN structures

Optimal length-constrained segmentation and subject-adaptive learning for real-time arrhythmia detection

© 2018 Association for Computing Machinery. An algorithm of data segmentation with length constraints for each segment is presented and applied in the context of arrhythmia detection. The additivity property of the cost function for each segment yields the induction proof of the exact global optimal solution. The experiments were conducted on the MIT-BIH arrhythmia dataset with the heartbeat categories recommended by the ANSI/AAMI EC57:1998 standard. The heartbeat classification task is enhanced by an adaptive learning scheme. Incremental support vector machine is used to integrate a small number of expert-annotated samples specific to the subject into the existing classifier previously learned from the dataset. The proposed segmentation scheme obtains the sensitivity of 99.89% and the positive predictivity of 99.83%. The classification sensitivities of ventricular and supraventricular detection are significantly boosted from 85.9% and 83.5% (subject-unadaptive) to 97.7% and 93.2% (subject-adaptive), respectively. Similarly the pre-dictivities increase from 94.8% to 99.3% (ventricular), and from 67.7% to 88.0% (supraventricular) when plugging in the adaptive learning method. The signal processing framework is conducted in a simulated real-time model. As compared to the previously reported studies we achieve a competitive performance in terms of all assessment measures

k-Nearest Neighbour Classifiers - A Tutorial

Perhaps the most straightforward classifier in the arsenal or Machine Learning techniques is the Nearest Neighbour Classifier – classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance because issues of poor run-time performance is not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours and mechanisms for reducing the dimension of the data.This paper is the second edition of a paper previously published as a technical report . Sections on similarity measures for time-series, retrieval speed-up and intrinsic dimensionality have been added. An Appendix is included providing access to Python code for the key methods

Explain what you see:argumentation-based learning and robotic vision

In this thesis, we have introduced new techniques for the problems of open-ended learning, online incremental learning, and explainable learning. These methods have applications in the classification of tabular data, 3D object category recognition, and 3D object parts segmentation. We have utilized argumentation theory and probability theory to develop these methods. The first proposed open-ended online incremental learning approach is Argumentation-Based online incremental Learning (ABL). ABL works with tabular data and can learn with a small number of learning instances using an abstract argumentation framework and bipolar argumentation framework. It has a higher learning speed than state-of-the-art online incremental techniques. However, it has high computational complexity. We have addressed this problem by introducing Accelerated Argumentation-Based Learning (AABL). AABL uses only an abstract argumentation framework and uses two strategies to accelerate the learning process and reduce the complexity. The second proposed open-ended online incremental learning approach is the Local Hierarchical Dirichlet Process (Local-HDP). Local-HDP aims at addressing two problems of open-ended category recognition of 3D objects and segmenting 3D object parts. We have utilized Local-HDP for the task of object part segmentation in combination with AABL to achieve an interpretable model to explain why a certain 3D object belongs to a certain category. The explanations of this model tell a user that a certain object has specific object parts that look like a set of the typical parts of certain categories. Moreover, integrating AABL and Local-HDP leads to a model that can handle a high degree of occlusion