Mid-level concept learning with visual contextual ontologies and probabilistic inference for image annotation

To date, automatic recognition of semantic information such as salient objects and mid-level concepts from images is a challenging task. Since real-world objects tend to exist in a context within their environment, the computer vision researchers have increasingly incorporated contextual information for improving object recognition. In this paper, we present a method to build a visual contextual ontology from salient objects descriptions for image annotation. The ontologies include not only partOf/kindOf relations, but also spatial and co-occurrence relations. A two-step image annotation algorithm is also proposed based on ontology relations and probabilistic inference. Different from most of the existing work, we specially exploit how to combine representation of ontology, contextual knowledge and probabilistic inference. The experiments show that image annotation results are improved in the LabelMe dataset.

Incorporating interdependence in risk likelihood analysis to enhance diagnostics in condition monitoring

This research is aimed at addressing problems in the field of asset management relating to risk analysis and decision making based on data from a Supervisory Control and Data Acquisition (SCADA) system. It is apparent that determining risk likelihood in risk analysis is difficult, especially when historical information is unreliable. This relates to a problem in SCADA data analysis because of nested data. A further problem is in providing beneficial information from a SCADA system to a managerial level information system (e.g. Enterprise Resource Planning/ERP). A Hierarchical Model is developed to address the problems. The model is composed of three different Analyses: Hierarchical Analysis, Failure Mode and Effect Analysis, and Interdependence Analysis. The significant contributions from the model include: (a) a new risk analysis model, namely an Interdependence Risk Analysis Model which does not rely on the existence of historical information because it utilises Interdependence Relationships to determine the risk likelihood, (b) improvement of the SCADA data analysis problem by addressing the nested data problem through the Hierarchical Analysis, and (c) presentation of a framework to provide beneficial information from SCADA systems to ERP systems. The case study of a Water Treatment Plant is utilised for model validation.

Recombinant protein production using a Tobacco yellow dwarf virus-based episomal expression vector : control of Rep activity

Over the past decade, plants have been used as expression hosts for the production of pharmaceutically important and commercially valuable proteins. Plants offer many advantages over other expression systems such as lower production costs, rapid scale up of production, similar post-translational modification as animals and the low likelihood of contamination with animal pathogens, microbial toxins or oncogenic sequences. However, improving recombinant protein yield remains one of the greatest challenges to molecular farming. In-Plant Activation (InPAct) is a newly developed technology that offers activatable and high-level expression of heterologous proteins in plants. InPAct vectors contain the geminivirus cis elements essential for rolling circle replication (RCR) and are arranged such that the gene of interest is only expressed in the presence of the cognate viral replication-associated protein (Rep). The expression of Rep in planta may be controlled by a tissue-specific, developmentally regulated or chemically inducible promoter such that heterologous protein accumulation can be spatially and temporally controlled. One of the challenges for the successful exploitation of InPAct technology is the control of Rep expression as even very low levels of this protein can reduce transformation efficiency, cause abnormal phenotypes and premature activation of the InPAct vector in regenerated plants. Tight regulation over transgene expression is also essential if expressing cytotoxic products. Unfortunately, many tissue-specific and inducible promoters are unsuitable for controlling expression of Rep due to low basal activity in the absence of inducer or in tissues other than the target tissue. This PhD aimed to control Rep activity through the production of single chain variable fragments (scFvs) specific to the motif III of Tobacco yellow dwarf virus (TbYDV) Rep. Due to the important role played by the conserved motif III in the RCR, it was postulated that such scFvs can be used to neutralise the activity of the low amount of Rep expressed from a “leaky” inducible promoter, thus preventing activation of the TbYDV-based InPAct vector until intentional induction. Such scFvs could also offer the potential to confer partial or complete resistance to TbYDV, and possibly heterologous viruses as motif III is conserved between geminiviruses. Studies were first undertaken to determine the levels of TbYDV Rep and TbYDV replication-associated protein A (RepA) required for optimal transgene expression from a TbYDV-based InPAct vector. Transient assays in a non-regenerable Nicotiana tabacum (NT-1) cell line were undertaken using a TbYDV-based InPAct vector containing the uidA reporter gene (encoding GUS) in combination with TbYDV Rep and RepA under the control of promoters with high (CaMV 35S) or low (Banana bunchy top virus DNA-R, BT1) activity. The replication enhancer protein of Tomato leaf curl begomovirus (ToLCV), REn, was also used in some co-bombardment experiments to examine whether RepA could be substituted by a replication enhancer from another geminivirus genus. GUS expression was observed both quantitatively and qualitatively by fluorometric and histochemical assays, respectively. GUS expression from the TbYDV-based InPAct vector was found to be greater when Rep was expected to be expressed at low levels (BT1 promoter) rather than high levels (35S promoter). GUS expression was further enhanced when Rep and RepA were co-bombarded with a low ratio of Rep to RepA. Substituting TbYDV RepA with ToLCV REn also enhanced GUS expression but more importantly highest GUS expression was observed when cells were co-transformed with expression vectors directing low levels of Rep and high levels of RepA irrespective of the level of REn. In this case, GUS expression was approximately 74-fold higher than that from a non-replicating vector. The use of different terminators, namely CaMV 35S and Nos terminators, in InPAct vectors was found to influence GUS expression. In the presence of Rep, GUS expression was greater using pInPActGUS-Nos rather than pInPActGUS-35S. The only instance of GUS expression being greater from vectors containing the 35S terminator was when comparing expression from cells transformed with Rep, RepA and REnexpressing vectors and either non-replicating vectors, p35SGS-Nos or p35SGS-35S. This difference was most likely caused by an interaction of viral replication proteins with each other and the terminators. These results indicated that (i) the level of replication associated proteins is critical to high transgene expression, (ii) the choice of terminator within the InPAct vector may affect expression levels and (iii) very low levels of Rep can activate InPAct vectors hence controlling its activity is critical. Prior to generating recombinant scFvs, a recombinant TbYDV Rep was produced in E. coli to act as a control to enable the screening for Rep-specific antibodies. A bacterial expression vector was constructed to express recombinant TbYDV Rep with an Nterminal His-tag (N-His-Rep). Despite investigating several purification techniques including Ni-NTA, anion exchange, hydrophobic interaction and size exclusion chromatography, N-His-Rep could only be partially purified using a Ni-NTA column under native conditions. Although it was not certain that this recombinant N-His-Rep had the same conformation as the native TbYDV Rep and was functional, results from an electromobility shift assay (EMSA) showed that N-His-Rep was able to interact with the TbYDV LIR and was, therefore, possibly functional. Two hybridoma cell lines from mice, immunised with a synthetic peptide containing the TbYDV Rep motif III amino acid sequence, were generated by GenScript (USA). Monoclonal antibodies secreted by the two hybridoma cell lines were first screened against denatured N-His-Rep in Western analysis. After demonstrating their ability to bind N-His-Rep, two scFvs (scFv1 and scFv2) were generated using a PCR-based approach. Whereas the variable heavy chain (VH) from both cell lines could be amplified, only the variable light chain (VL) from cell line 2 was amplified. As a result, scFv1 contained VH and VL from cell line 1, whereas scFv2 contained VH from cell line 2 and VL from cell line 1. Both scFvs were first expressed in E. coli in order to evaluate their affinity to the recombinant TbYDV N-His-Rep. The preliminary results demonstrated that both scFvs were able to bind to the denatured N-His-Rep. However, EMSAs revealed that only scFv2 was able to bind to native N-His-Rep and prevent it from interacting with the TbYDV LIR. Each scFv was cloned into plant expression vectors and co-bombarded into NT-1 cells with the TbYDV-based InPAct GUS expression vector and pBT1-Rep to examine whether the scFvs could prevent Rep from mediating RCR. Although it was expected that the addition of the scFvs would result in decreased GUS expression, GUS expression was found to slightly increase. This increase was even more pronounced when the scFvs were targeted to the cell nucleus by the inclusion of the Simian virus 40 large T antigen (SV40) nuclear localisation signal (NLS). It was postulated that the scFvs were binding to a proportion of Rep, leaving a small amount available to mediate RCR. The outcomes of this project provide evidence that very high levels of recombinant protein can theoretically be expressed using InPAct vectors with judicious selection and control of viral replication proteins. However, the question of whether the scFvs generated in this project have sufficient affinity for TbYDV Rep to prevent its activity in a stably transformed plant remains unknown. It may be that other scFvs with different combinations of VH and VL may have greater affinity for TbYDV Rep. Such scFvs, when expressed at high levels in planta, might also confer resistance to TbYDV and possibly heterologous geminiviruses.

Best basis-based intelligent machine fault diagnosis

The wavelet packet transform decomposes a signal into a set of bases for time–frequency analysis. This decomposition creates an opportunity for implementing distributed data mining where features are extracted from different wavelet packet bases and served as feature vectors for applications. This paper presents a novel approach for integrated machine fault diagnosis based on localised wavelet packet bases of vibration signals. The best basis is firstly determined according to its classification capability. Data mining is then applied to extract features and local decisions are drawn using Bayesian inference. A final conclusion is reached using a weighted average method in data fusion. A case study on rolling element bearing diagnosis shows that this approach can greatly improve the accuracy ofdiagno sis.

The effect of dialect mismatch on likelihood-maximising speech enhancement for noise-robust speech recognition

Traditional speech enhancement methods optimise signal-level criteria such as signal-to-noise ratio, but these approaches are sub-optimal for noise-robust speech recognition. Likelihood-maximising (LIMA) frameworks are an alternative that optimise parameters of enhancement algorithms based on state sequences generated for utterances with known transcriptions. Previous reports of LIMA frameworks have shown significant promise for improving speech recognition accuracies under additive background noise for a range of speech enhancement techniques. In this paper we discuss the drawbacks of the LIMA approach when multiple layers of acoustic mismatch are present – namely background noise and speaker accent. Experimentation using LIMA-based Mel-filterbank noise subtraction on American and Australian English in-car speech databases supports this discussion, demonstrating that inferior speech recognition performance occurs when a second layer of mismatch is seen during evaluation.

The application of phonetic distribution normalisation to likelihood-maximising speech enhancement for robust ASR

Traditional speech enhancement methods optimise signal-level criteria such as signal-to-noise ratio, but such approaches are sub-optimal for noise-robust speech recognition. Likelihood-maximising (LIMA) frameworks on the other hand, optimise the parameters of speech enhancement algorithms based on state sequences generated by a speech recogniser for utterances of known transcriptions. Previous applications of LIMA frameworks have generated a set of global enhancement parameters for all model states without taking in account the distribution of model occurrence, making optimisation susceptible to favouring frequently occurring models, in particular silence. In this paper, we demonstrate the existence of highly disproportionate phonetic distributions on two corpora with distinct speech tasks, and propose to normalise the influence of each phone based on a priori occurrence probabilities. Likelihood analysis and speech recognition experiments verify this approach for improving ASR performance in noisy environments.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

Estimation of parameters for macroparasite population evolution using approximate Bayesian computation

We estimate the parameters of a stochastic process model for a macroparasite population within a host using approximate Bayesian computation (ABC). The immunity of the host is an unobserved model variable and only mature macroparasites at sacrifice of the host are counted. With very limited data, process rates are inferred reasonably precisely. Modeling involves a three variable Markov process for which the observed data likelihood is computationally intractable. ABC methods are particularly useful when the likelihood is analytically or computationally intractable. The ABC algorithm we present is based on sequential Monte Carlo, is adaptive in nature, and overcomes some drawbacks of previous approaches to ABC. The algorithm is validated on a test example involving simulated data from an autologistic model before being used to infer parameters of the Markov process model for experimental data. The fitted model explains the observed extra-binomial variation in terms of a zero-one immunity variable, which has a short-lived presence in the host.

Ontology-based image annotation

With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted its emphasis from low-level features analysis to high-level image semantics extrac- tion. User studies reveal that users tend to seek information using high-level semantics. Therefore, image semantics extraction is of great importance to content-based image retrieval because it allows the users to freely express what images they want. Semantic content annotation is the basis for semantic content retrieval. The aim of image anno- tation is to automatically obtain keywords that can be used to represent the content of images. The major research challenges in image semantic annotation are: what is the basic unit of semantic representation? how can the semantic unit be linked to high-level image knowledge? how can the contextual information be stored and utilized for image annotation? In this thesis, the Semantic Web technology (i.e. ontology) is introduced to the image semantic annotation problem. Semantic Web, the next generation web, aims at mak- ing the content of whatever type of media not only understandable to humans but also to machines. Due to the large amounts of multimedia data prevalent on the Web, re- searchers and industries are beginning to pay more attention to the Multimedia Semantic Web. The Semantic Web technology provides a new opportunity for multimedia-based applications, but the research in this area is still in its infancy. Whether ontology can be used to improve image annotation and how to best use ontology in semantic repre- sentation and extraction is still a worth-while investigation. This thesis deals with the problem of image semantic annotation using ontology and machine learning techniques in four phases as below. 1) Salient object extraction. A salient object servers as the basic unit in image semantic extraction as it captures the common visual property of the objects. Image segmen- tation is often used as the �rst step for detecting salient objects, but most segmenta- tion algorithms often fail to generate meaningful regions due to over-segmentation and under-segmentation. We develop a new salient object detection algorithm by combining multiple homogeneity criteria in a region merging framework. 2) Ontology construction. Since real-world objects tend to exist in a context within their environment, contextual information has been increasingly used for improving object recognition. In the ontology construction phase, visual-contextual ontologies are built from a large set of fully segmented and annotated images. The ontologies are composed of several types of concepts (i.e. mid-level and high-level concepts), and domain contextual knowledge. The visual-contextual ontologies stand as a user-friendly interface between low-level features and high-level concepts. 3) Image objects annotation. In this phase, each object is labelled with a mid-level concept in ontologies. First, a set of candidate labels are obtained by training Support Vectors Machines with features extracted from salient objects. After that, contextual knowledge contained in ontologies is used to obtain the �nal labels by removing the ambiguity concepts. 4) Scene semantic annotation. The scene semantic extraction phase is to get the scene type by using both mid-level concepts and domain contextual knowledge in ontologies. Domain contextual knowledge is used to create scene con�guration that describes which objects co-exist with which scene type more frequently. The scene con�guration is represented in a probabilistic graph model, and probabilistic inference is employed to calculate the scene type given an annotated image. To evaluate the proposed methods, a series of experiments have been conducted in a large set of fully annotated outdoor scene images. These include a subset of the Corel database, a subset of the LabelMe dataset, the evaluation dataset of localized semantics in images, the spatial context evaluation dataset, and the segmented and annotated IAPR TC-12 benchmark.

Multivariate Markov Process Models for the transmission of methicillin-resistant Staphylococcus Aureus in a hospital ward

Methicillin-resistant Staphylococcus Aureus (MRSA) is a pathogen that continues to be of major concern in hospitals. We develop models and computational schemes based on observed weekly incidence data to estimate MRSA transmission parameters. We extend the deterministic model of McBryde, Pettitt, and McElwain (2007, Journal of Theoretical Biology 245, 470–481) involving an underlying population of MRSA colonized patients and health-care workers that describes, among other processes, transmission between uncolonized patients and colonized health-care workers and vice versa. We develop new bivariate and trivariate Markov models to include incidence so that estimated transmission rates can be based directly on new colonizations rather than indirectly on prevalence. Imperfect sensitivity of pathogen detection is modeled using a hidden Markov process. The advantages of our approach include (i) a discrete valued assumption for the number of colonized health-care workers, (ii) two transmission parameters can be incorporated into the likelihood, (iii) the likelihood depends on the number of new cases to improve precision of inference, (iv) individual patient records are not required, and (v) the possibility of imperfect detection of colonization is incorporated. We compare our approach with that used by McBryde et al. (2007) based on an approximation that eliminates the health-care workers from the model, uses Markov chain Monte Carlo and individual patient data. We apply these models to MRSA colonization data collected in a small intensive care unit at the Princess Alexandra Hospital, Brisbane, Australia.

Continuous appearance-based trajectory SLAM

This paper describes a novel probabilistic approach to incorporating odometric information into appearance-based SLAM systems, without performing metric map construction or calculating relative feature geometry. The proposed system, dubbed Continuous Appearance-based Trajectory SLAM (CAT-SLAM), represents location as a probability distribution along a trajectory, and represents appearance continuously over the trajectory rather than at discrete locations. The distribution is evaluated using a Rao-Blackwellised particle filter, which weights particles based on local appearance and odometric similarity and explicitly models both the likelihood of revisiting previous locations and visiting new locations. A modified resampling scheme counters particle deprivation and allows loop closure updates to be performed in constant time regardless of map size. We compare the performance of CAT-SLAM to FAB-MAP (an appearance-only SLAM algorithm) in an outdoor environment, demonstrating a threefold increase in the number of correct loop closures detected by CAT-SLAM.

Bayes Factor based speaker segmentation for speaker diarization

This paper proposes the use of the Bayes Factor as a distance metric for speaker segmentation within a speaker diarization system. The proposed approach uses a pair of constant sized, sliding windows to compute the value of the Bayes Factor between the adjacent windows over the entire audio. Results obtained on the 2002 Rich Transcription Evaluation dataset show an improved segmentation performance compared to previous approaches reported in literature using the Generalized Likelihood Ratio. When applied in a speaker diarization system, this approach results in a 5.1% relative improvement in the overall Diarization Error Rate compared to the baseline.

Critical gap estimation by numerical and statistical highest likelihood search

Many traffic situations require drivers to cross or merge into a stream having higher priority. Gap acceptance theory enables us to model such processes to analyse traffic operation. This discussion demonstrated that numerical search fine tuned by statistical analysis can be used to determine the most likely critical gap for a sample of drivers, based on their largest rejected gap and accepted gap. This method shares some common features with the Maximum Likelihood Estimation technique (Troutbeck 1992) but lends itself well to contemporary analysis tools such as spreadsheet and is particularly analytically transparent. This method is considered not to bias estimation of critical gap due to very small rejected gaps or very large rejected gaps. However, it requires a sufficiently large sample that there is reasonable representation of largest rejected gap/accepted gap pairs within a fairly narrow highest likelihood search band.