Comparing dissimilarity measures for content-based image retrieval

Dissimilarity measurement plays a crucial role in content-based image retrieval, where data objects and queries are represented as vectors in high-dimensional content feature spaces. Given the large number of dissimilarity measures that exist in many fields, a crucial research question arises: Is there a dependency, if yes, what is the dependency, of a dissimilarity measure’s retrieval performance, on different feature spaces? In this paper, we summarize fourteen core dissimilarity measures and classify them into three categories. A systematic performance comparison is carried out to test the effectiveness of these dissimilarity measures with six different feature spaces and some of their combinations on the Corel image collection. From our experimental results, we have drawn a number of observations and insights on dissimilarity measurement in content-based image retrieval, which will lay a foundation for developing more effective image search technologies.

Improved data visualisation through multiple dissimilarity modelling

Popular dimension reduction and visualisation algorithms rely on the assumption that input dissimilarities are typically Euclidean, for instance Metric Multidimensional Scaling, t-distributed Stochastic Neighbour Embedding and the Gaussian Process Latent Variable Model. It is well known that this assumption does not hold for most datasets and often high-dimensional data sits upon a manifold of unknown global geometry. We present a method for improving the manifold charting process, coupled with Elastic MDS, such that we no longer assume that the manifold is Euclidean, or of any particular structure. We draw on the benefits of different dissimilarity measures allowing for the relative responsibilities, under a linear combination, to drive the visualisation process.

Multinationality and performance: The role of cultural dissimilarity

The purpose of this research was to examine the influence of cultural dissimilarity on the relationship between multinationality and performance. Both direct and indirect effects were studied. In addition, the form of the multinationality-performance relationship was investigated.^ Five indicators of cultural dissimilarity were developed on the basis of Hofstede's cultural dimensions. Performance was measured along two dimensions--financial and operational. Multinationality was operationalized as the ratio of foreign sales to total sales. Secondary data was used for all variables in the study. The sample of firms comprised multinationals based in the United States from four global industries--chemicals, computers and office equipment, electrical and electrical goods, and drugs and pharmaceuticals.^ Regression analyses using pooled cross-section/time-series data indicated that the relationship between multinationality and performance is curvilinear. No direct effects of cultural dissimilarity on performance were found. However, the results show a moderating effect of cultural dissimilarity on the multinationality-performance relationship. The direction of this effect was positive for four of the five cultural dissimilarity measures. ^

Comparing large graphs efficiently by margins of feature vectors

Measuring the structural similarity of graphs is a challenging and outstanding problem. Most of the classical approaches of the so-called exact graph matching methods are based on graph or subgraph isomorphic relations of the underlying graphs. In contrast to these methods in this paper we introduce a novel approach to measure the structural similarity of directed and undirected graphs that is mainly based on margins of feature vectors representing graphs. We introduce novel graph similarity and dissimilarity measures, provide some properties and analyze their algorithmic complexity. We find that the computational complexity of our measures is polynomial in the graph size and, hence, significantly better than classical methods from, e.g. exact graph matching which are NP-complete. Numerically, we provide some examples of our measure and compare the results with the well-known graph edit distance. (c) 2006 Elsevier Inc. All rights reserved.

Making the most of the self-quotient image in face recognition

The self-quotient image is a biologically inspired representation which has been proposed as an illumination invariant feature for automatic face recognition. Owing to the lack of strong domain specific assumptions underlying this representation, it can be readily extracted from raw images irrespective of the persons's pose, facial expression etc. What makes the self-quotient image additionally attractive is that it can be computed quickly and in a closed form using simple low-level image operations. However, it is generally accepted that the self-quotient is insufficiently robust to large illumination changes which is why it is mainly used in applications in which low precision is an acceptable compromise for high recall (e.g. retrieval systems). Yet, in this paper we demonstrate that the performance of this representation in challenging illuminations has been greatly underestimated. We show that its error rate can be reduced by over an order of magnitude, without any changes to the representation itself. Rather, we focus on the manner in which the dissimilarity between two self-quotient images is computed. By modelling the dominant sources of noise affecting the representation, we propose and evaluate a series of different dissimilarity measures, the best of which reduces the initial error rate of 63.0% down to only 5.7% on the notoriously challenging YaleB data set.

Biosystematics of the Genus Andryala L. (Asteraceae)

Andryala (Asteraceae: Cichorieae) is a little-known Mediterranean-Macaronesian genus whose taxonomy is much in need of revision. The aim of the present biosystematic study was to elucidate species relationships within this genus based on morphological and molecular data. In this study several taxa are recognised: 17 species, 14 subspecies, and 3 hybrids. Among these, 5 species are Macaronesian endemics (A. glandulosa, A. sparsiflora, A. crithmifolia Aiton, A. pinnatifida, and A. perezii), 4 species are Northwest African endemics (A. mogadorensis, A. maroccana, A. chevallieri, and A. nigricans) and one species is endemic to Romania (A. laevitomentosa). Historical background regarding taxonomic delimitation in the genus is addressed from Linnaean to present day concepts, as well as the origin of the name Andryala. The origin of Asteraceae and the systematic position of Andryala is shortly summarised. The morphological study was based on a bibliographic review and the revision of 1066 specimens of 13 herbaria as well as additional material collected during fieldwork. The variability of the morphological characters of the genus, including both vegetative taxonomic characters (root, stem, leaf and indumentum characters) and reproductive ones (inflorescence, floret, fruit and pappus characters), is assessed. Numerical analysis of the morphological data was performed using different similarity or dissimilarity measures and coefficients, as well as ordination and clustering methods. Results support the segregation of the recognised taxa and the congruence of the several analyses in the separation of the recognised taxa (using quantitative, binary or multi-state characters). The proposed taxonomy for Andryala includes a new infra-generic classification, new taxa and new combinations and ranks, typifications and diagnostic keys (one for the species and several for subspecies). For each taxon a list of synonyms, typification comments and a detailed description are provided, just as comments on taxonomy and nomenclature, and a brief discussion on karyology. Additionally, information on ecology and conservation status as well as on distribution and a list of studied material are also presented. Phylogenetic analyses based on different nuclear and chloroplast DNA markers, using Bayesian and maximum parsimony methods of inference, were performed. Results support three main lineages: separate ones for the relict species A. agardhii and A. laevitomentosa and a third including the majority of the Andryala species that underwent a relatively rapid and recent speciation. They also suggest a single colonization event of Madeira and the Canary Islands from the Mediterranean region, followed by insular speciation. Biogeography and speciation within the genus are briefly discussed, including a proposal for the centre of origin of the genus and possible dispersal routes.

Comparação de métodos de agrupamento para o estudo da divergência genética em cultivares de feijão

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Biosystematics of the Genus Andryala L. (Asteraceae)

Andryala (Asteraceae: Cichorieae) is a little-known Mediterranean-Macaronesian genus whose taxonomy is much in need of revision. The aim of the present biosystematic study was to elucidate species relationships within this genus based on morphological and molecular data. In this study several taxa are recognised: 17 species, 14 subspecies, and 3 hybrids. Among these, 5 species are Macaronesian endemics (A. glandulosa, A. sparsiflora, A. crithmifolia Aiton, A. pinnatifida, and A. perezii), 4 species are Northwest African endemics (A. mogadorensis, A. maroccana, A. chevallieri, and A. nigricans) and one species is endemic to Romania (A. laevitomentosa). Historical background regarding taxonomic delimitation in the genus is addressed from Linnaean to present day concepts, as well as the origin of the name Andryala. The origin of Asteraceae and the systematic position of Andryala is shortly summarised. The morphological study was based on a bibliographic review and the revision of 1066 specimens of 13 herbaria as well as additional material collected during fieldwork. The variability of the morphological characters of the genus, including both vegetative taxonomic characters (root, stem, leaf and indumentum characters) and reproductive ones (inflorescence, floret, fruit and pappus characters), is assessed. Numerical analysis of the morphological data was performed using different similarity or dissimilarity measures and coefficients, as well as ordination and clustering methods. Results support the segregation of the recognised taxa and the congruence of the several analyses in the separation of the recognised taxa (using quantitative, binary or multi-state characters). The proposed taxonomy for Andryala includes a new infra-generic classification, new taxa and new combinations and ranks, typifications and diagnostic keys (one for the species and several for subspecies). For each taxon a list of synonyms, typification comments and a detailed description are provided, just as comments on taxonomy and nomenclature, and a brief discussion on karyology. Additionally, information on ecology and conservation status as well as on distribution and a list of studied material are also presented. Phylogenetic analyses based on different nuclear and chloroplast DNA markers, using Bayesian and maximum parsimony methods of inference, were performed. Results support three main lineages: separate ones for the relict species A. agardhii and A. laevitomentosa and a third including the majority of the Andryala species that underwent a relatively rapid and recent speciation. They also suggest a single colonization event of Madeira and the Canary Islands from the Mediterranean region, followed by insular speciation. Biogeography and speciation within the genus are briefly discussed, including a proposal for the centre of origin of the genus and possible dispersal routes.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

The Queensland study of melanoma : Environmental and genetic associations (Q-MEGA); Study design, baseline characteristics, and repeatability of phenotype and sun exposure measures

Cutaneous malignant melanoma (CMM) is a major health issue in Queensland, Australia, which has the world’s highest incidence. Recent molecular and epidemiologic studies suggest that CMM arises through multiple etiological pathways involving gene-environment interactions. Understanding the potential mechanisms leading to CMM requires larger studies than those previously conducted. This article describes the design and baseline characteristics of Q-MEGA, the Queensland Study of Melanoma: Environmental and Genetic Associations, which followed up 4 population-based samples of CMM patients in Queensland, including children, adolescents, men aged over 50, and a large sample of adult cases and their families, including twins. Q-MEGA aims to investigate the roles of genetic and environmental factors, and their interaction, in the etiology of melanoma. Three thousand, four hundred and seventy-one participants took part in the follow-up study and were administered a computer-assisted telephone interview in 2002-2005. Updated data on environmental and phenotypic risk factors, and 2777 blood samples were collected from interviewed participants as well as a subset of relatives. This study provides a large and well-described population-based sample of CMM cases with follow-up data. Characteristics of the cases and repeatability of sun exposure and phenotype measures between the baseline and the follow-up surveys, from 6 to 17 years later, are also described.

Development of self-efficacy and expectancy measures for benzodiazepines

This study aimed to develop and assess the reliability and validity of a pair of self-report questionnaires to measure self-efficacy and expectancy associated with benzodiazepine use, the Benzodiazepine Refusal Self- Efficacy Questionnaire (BRSEQ) and the Benzodiazepine Expectancy Questionnaire (BEQ). Internal structure of the questionnaireswas established by principal component analysis (PCA) in a sample of 155 respondents, and verified by confirmatory factor analyses (CFA) in a second independent sample (n=139) using structural equation modeling. The PCA of the BRSEQ resulted in a 16-item, 4-factor scale, and the BEQ formed an 18-item, 2-factor scale. Both scales were internally reliable. CFA confirmed these internal structures and reduced the questionnaires to a 14-item self-efficacy scale and a 12-item expectancy scale. Lower self-efficacy and higher expectancy were moderately associated with higher scores on the SDS-B. The scales provide reliable measures for assessing benzodiazepine self-efficacy and expectancies. Future research will examine the utility of the scales in prospective prediction of benzodiazepine cessation.

Cognitive assessment in closed head injury : stability, validity and parallel forms for two neuropsychological measures of recovery

Investigated the psychometric properties of the original and alternate sets of the Trail Making Test (TMT) and the Controlled Oral Word Association Test (COWAT; A. L. Benton and D. Hamsher, 1978) in 50 orthopedic and 15 closed head injured (1 yr after trauma) patients (aged 15–59 yrs). Although the alternate forms of both measures proved to be stable and consistent with each other in both groups, only the parallel sets of TMT reliably discriminated the clinical group from controls. Practice effects in the head injured were significant only for Trail B of TMT. Factor analysis of the control group's results identified Verbal Knowledge as a major contributor to performance on COWAT, whereas TMT was more dependent on Rapid Visual Search and Visuomotor Sequencing.