Adaptive combinations of classifiers with application to on-line handwritten character recognition

Classifier combining is an effective way of improving classification performance. User adaptation is clearly another valid approach for improving performance in a user-dependent system, and even though adaptation is usually performed on the classifier level, also adaptive committees can be very effective. Adaptive committees have the distinct ability of performing adaptation without detailed knowledge of the classifiers. Adaptation can therefore be used even with classification systems that intrinsically are not suited for adaptation, whether that be due to lack of access to the workings of the classifier or simply a classification scheme not suitable for continuous learning. This thesis proposes methods for adaptive combination of classifiers in the setting of on-line handwritten character recognition. The focal part of the work introduces adaptive classifier combination schemes, of which the two most prominent ones are the Dynamically Expanding Context (DEC) committee and the Class-Confidence Critic Combining (CCCC) committee. Both have been shown to be capable of successful adaptation to the user in the task of on-line handwritten character recognition. Particularly the highly modular CCCC framework has shown impressive performance also in a doubly-adaptive setting of combining adaptive classifiers by using an adaptive committee. In support of this main topic of the thesis, some discussion on a methodology for deducing correct character labeling from user actions is presented. Proper labeling is paramount for effective adaptation, and deducing the labels from the user's actions is necessary to perform adaptation transparently to the user. In that way, the user does not need to give explicit feedback on the correctness of the recognition results. Also, an overview is presented of adaptive classification methods for single-classifier adaptation in handwritten character recognition developed at the Laboratory of Computer and Information Science of the Helsinki University of Technology, CIS-HCR. Classifiers based on the CIS-HCR system have been used in the adaptive committee experiments as both member classifiers and to provide a reference level. Finally, two distinct approaches for improving the performance of committee classifiers further are discussed. Firstly, methods for committee rejection are presented and evaluated. Secondly, measures of classifier diversity for classifier selection, based on the concept of diversity of errors, are presented and evaluated. The topic of this thesis hence covers three important aspects of pattern recognition: on-line adaptation, combining classifiers, and a practical evaluation setting of handwritten character recognition. A novel approach combining these three core ideas has been developed and is presented in the introductory text and the included publications. To reiterate, the main contributions of this thesis are: 1) introduction of novel adaptive committee classification methods, 2) introduction of novel methods for measuring classifier diversity, 3) presentation of some methods for implementing committee rejection, 4) discussion and introduction of a method for effective label deduction from on-line user actions, and as a side-product, 5) an overview of the CIS-HCR adaptive on-line handwritten character recognition system.Luokittimien yhdistäminen komitealuokittimella on tehokas keino luokitustarkkuuden parantamiseen. Laskentatehon jatkuva kasvu tekee myös useiden luokittimien yhtäaikaisesta käytöstä yhä varteenotettavamman vaihtoehdon. Järjestelmän adaptoituminen (mukautuminen) käyttäjään on toinen hyvä keino käyttäjäriippumattoman järjestelmän tarkkuuden parantantamiseksi. Vaikka adaptaatio yleensä toteutetaan luokittimen tasolla, myös adaptiiviset komitealuokittimet voivat olla hyvin tehokkaita. Adaptiiviset komiteat voivat adaptoitua ilman yksityiskohtaista tietoa jäsenluokittimista. Adaptaatiota voidaan näin käyttää myös luokittelujärjestelmissä, jotka eivät ole itsessään sopivia adaptaatioon. Adaptaatioon sopimattomuus voi johtua esimerkiksi siitä, että luokittimen totetutusta ei voida muuttaa, tai siitä, että käytetään luokittelumenetelmää, joka ei sovellu jatkuvaan oppimiseen. Tämä väitöskirja käsittelee menetelmiä luokittimien adaptiiviseen yhdistämiseen käyttäen sovelluskohteena käsinkirjoitettujen merkkien on-line-tunnistusta. Keskeisin osa työtä esittelee uusia adaptiivisia luokittimien yhdistämismenetelmiä, joista kaksi huomattavinta ovat Dynamically Expanding Context (DEC) -komitea sekä Class-Confidence Critic Combining (CCCC) -komitea. Molemmat näistä ovat osoittautuneet kykeneviksi tehokkaaseen käyttäjä-adaptaatioon käsinkirjoitettujen merkkien on-line-tunnistuksessa. Erityisesti hyvin modulaarisella CCCC järjestelmällä on saatu hyviä tuloksia myös kaksinkertaisesti adaptiivisessa asetelmassa, jossa yhdistetään adaptiivisia jäsenluokittimia adaptiivisen komitean avulla. Väitöskirjan pääteeman tukena esitetään myös malli ja käytännön esimerkki siitä, miten käyttäjän toimista merkeille voidaan päätellä oikeat luokat. Merkkien todellisen luokan onnistunut päättely on elintärkeää tehokkaalle adaptaatiolle. Jotta adaptaatio voitaisiin suorittaa käyttäjälle läpinäkyvästi, merkkien todelliset luokat on kyettävä päättelemään käyttäjän toimista. Tällä tavalla käyttäjän ei tarvitse antaa suoraa palautetta tunnistustuloksen oikeellisuudesta. Työssä esitetään myös yleiskatsaus Teknillisen korkeakoulun Informaatiotekniikan laboratoriossa kehitettyyn adaptiiviseen käsinkirjoitettujen merkkien tunnistusjärjestelmään. Tähän järjestelmään perustuvia luokittimia on käytetty adaptiivisten komitealuokittimien kokeissa sekä jäsenluokittimina että vertailutasona. Lopuksi esitellään kaksi erillistä menetelmää komitealuokittimen tarkkuuden edelleen parantamiseksi. Näistä ensimmäinen on joukko menetelmiä komitealuokittimen rejektion (hylkäyksen) toteuttamiseksi. Toinen esiteltävä menetelmä on käyttää luokittimien erilaisuuden mittoja jäsenluokittimien valintaa varten. Ehdotetut uudet erilaisuusmitat perustuvat käsitteeseen, jota kutsumme virheiden erilaisuudeksi. Väitöskirjan aihe kattaa kolme hahmontunnistuksen tärkeää osa-aluetta: online-adaptaation, luokittimien yhdistämisen ja käytännön sovellusalana käsinkirjoitettujen merkkien tunnistuksen. Näistä kolmesta lähtökohdasta on kehitetty uudenlainen synteesi, joka esitetään johdantotekstissä sekä liitteenä olevissa julkaisuissa. Tämän väitöskirjan oleellisimmat kontribuutiot ovat siten: 1) uusien adaptiivisten komitealuokittimien esittely, 2) uudenlaisten menetelmien esittely luokittimien erilaisuuden mittaamiseksi, 3) joidenkin komitearejektiomenetelmien esittely, 4) pohdinnan ja erään toteutustavan esittely syötettyjen merkkien todellisen luokan päättelemiseksi käyttäjän toimista, sekä sivutuotteena 5) kattava yleiskatsaus CIS-HCR adaptiiviseen on-line käsinkirjoitettujen merkkien tunnistusjärjestelmään.reviewe

Distorted Fingerprint Verification System

Fingerprint verification is one of the most reliable personal identification methods. Fingerprint matching is affected by non-linear distortion introduced in fingerprint impression during the image acquisition process. This non-linear deformation changes both the position and orientation of minutiae. The proposed system operates in three stages: alignment based fingerprint matching, fuzzy clustering and classifier framework. First, an enhanced input fingerprint image has been aligned with the template fingerprint image and matching score is computed. To improve the performance of the system, a fuzzy clustering based on distance and density has been used to cluster the feature set obtained from the fingerprint matcher. Finally a classifier framework has been developed and found that cost sensitive classifier produces better results. The system has been evaluated on fingerprint database and the experimental result shows that system produces a verification rate of 96%. This system plays an important role in forensic and civilian applications.Biometric, Fingerprints, Distortion, Fuzzy Clustering, Cost Sensitive Classifier

Multitraining support vector machine for image retrieval

Relevance feedback (RF) schemes based on support vector machines (SVMs) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based RF approaches is often poor when the number of labeled feedback samples is small. This is mainly due to 1) the SVM classifier being unstable for small-size training sets because its optimal hyper plane is too sensitive to the training examples; and 2) the kernel method being ineffective because the feature dimension is much greater than the size of the training samples. In this paper, we develop a new machine learning technique, multitraining SVM (MTSVM), which combines the merits of the cotraining technique and a random sampling method in the feature space. Based on the proposed MTSVM algorithm, the above two problems can be mitigated. Experiments are carried out on a large image set of some 20 000 images, and the preliminary results demonstrate that the developed method consistently improves the performance over conventional SVM-based RFs in terms of precision and standard deviation, which are used to evaluate the effectiveness and robustness of a RF algorithm, respectively

A LightGBM-Based EEG Analysis Method for Driver Mental States Classification

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)

A Set of Criteria for Face Detection Preprocessing

AbstractThe goal of this paper is to provide a robust set of preprocessing steps to be used with any face detection system. Usually, the purpose of using preprocessing steps in face detection system is to speed up the detection process and reducing false positives. A preprocessing step should reject an acceptable amount of non-face windows. First proposed criterion is based on linear image transform (LIT) which ignores scanning a number of non-face windows. Second criterion utilizes regional minima (RM) to reject non-face windows. The last one uses a modified adaptive thresholding (ADT) technique to convert input image into a binary representation and perform an exclusion process on the latter form. The proposed criteria have been used in conjunction with a version of Viola-Jones face detector. Experimental results show significant advantage against early exclusion criterion or variance classifier in terms of speed and rejection rate. CMU-MIT and BioID datasets have been used in the experiments

Bio-driven control system for the rehabilitation hand device : a new approach

University of Technology Sydney. Faculty of Engineering and Information Technology.The myoelectric pattern recognition (M-PR) for hand rehabilitation devices has shown its efficacy in the laboratory environment. However, the performance of the M-PR in the clinical application is very poor. There is a big gap between the success of the laboratory experiment and the clinical application. The researchers found that the major cause of the gap was the robustness of the M-PR. Many aspects influence the robustness of the M-PR including the limb position, skin humidity, muscle fatigue, improvement in the muscle function, electrode shifts, and other clinical reasons. The aim of this thesis is to introduce novel M-PRs dealing with the robustness issues in real-time implementation. The goal was accomplished through the following actions. 1. Developing a new M-PR that can work well on the amputees and non-amputees. The proposed M-PR consists of time-domain and autoregressive features (TD-AR), spectral regression discriminant analysis (SRDA) as a feature reducer, and radial basis function extreme learning (RBF-ELM) as a classifier. The experimental results showed that the proposed system was able to detect the user’s intention with accuracy of roughly 99% on the able-bodied subjects and around 98% on the trans-radial amputees using six EMG channels. 2. Introducing new classifiers. The first classifier is adaptive wavelet extreme machine learning (AW-ELM). AW-ELM is the node-based ELM that can adapt to the changes that occur in the input. In general, AW-ELM could classify ten finger movements from two EMG channels with a good accuracy of 94.84 %. The second classifier is swarm radial basis extreme learning machine (SRBF-ELM). SRBF-ELM is a hybridization of particle swarm optimization (PSO) and the kernel-based ELM. The role of PSO is to optimize the kernel parameters. The last classifier is swarm wavelet extreme learning machine (SW-RBF-ELM). The role of the wavelet is to avoid PSO being trapped in local optima. The experiments have been done on the healthy subjects and amputees for both, SRBF-ELM and SW-RBF-ELM. On the healthy subjects, the accuracy of SW-RBF-ELM is 95.62 % while SRBF-ELM is 95.53 %. On the amputees, the SW-RBF-ELM achieved the average accuracy of 94.27 %, while SRBF-ELM produced the average accuracy of 92.55 %. 3. Developing a new feature projection and feature reduction called spectral regression extreme learning (SR-ELM). SR-ELM can enhance the class separability of the features to improve the classification performance. The experimental results showed that SR-ELM can work well on different classifiers and various numbers of classes with an average accuracy ranging from 95.67 % to 86.73 % 4. Developing a robust M-PR by involving the transient state of EMG signal along with the steady state of it in the real-time experiment. The classification accuracy is 90.46 % and 89.19 % on the offline and online classification, respectively. 5. Introducing a new myoelectric controller for the exoskeleton hand. The myoelectric controller consists of two main parts: the myoelectric pattern recognition (M-PR) and myoelectric non-pattern recognition (M-non-PR). In the system, RBF-ELM-R (radial basis extreme learning machine with a rejection mechanism) represents the M-PR, and the proportional controller represents the M-non-PR. The power actuated to the linear motors is proportional to the amplitude of the EMG signals. The experimental results showed that, in the offline experiment of 10 classes, the accuracy is around 90 % and 92 % for RBF-ELM and RBF-ELM-R, respectively. In the online experiment, the accuracy is about 89.22 % and 89.73 % for RBF-ELM and RBF-ELM-R, respectively. 6. Introducing an adaptive mechanism to the M-PR to adapt to changes in the characteristic of the electromyography (EMG) signal. The thesis proposes a new M-PR with online sequential extreme learning machine (OS-ELM) and OS-ELM with rejection (OS-ELM-R). The experimental results showed that the accuracy is around 89 % and 91 % for OS-ELM and OS-ELM-R on the first-day experiment