Multi modal multi-semantic image retrieval

PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ ability to find and access the specific visual information of interest to them. In recent years, ontology knowledge-based (KB) image information retrieval techniques have been adopted into in order to attempt to extract knowledge from these images, enhancing the retrieval performance. A KB framework is presented to promote semi-automatic annotation and semantic image retrieval using multimodal cues (visual features and text captions). In addition, a hierarchical structure for the KB allows metadata to be shared that supports multi-semantics (polysemy) for concepts. The framework builds up an effective knowledge base pertaining to a domain specific image collection, e.g. sports, and is able to disambiguate and assign high level semantics to ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. Local features are more useful than global features, e.g. colour, shape or texture, as they are invariant to image scale, orientation and camera angle. An innovative approach is proposed for the representation, annotation and retrieval of visual content using a hybrid technique based upon the use of an unstructured visual word and upon a (structured) hierarchical ontology KB model. The structural model facilitates the disambiguation of unstructured visual words and a more effective classification of visual content, compared to a vector space model, through exploiting local conceptual structures and their relationships. The key contributions of this framework in using local features for image representation include: first, a method to generate visual words using the semantic local adaptive clustering (SLAC) algorithm which takes term weight and spatial locations of keypoints into account. Consequently, the semantic information is preserved. Second a technique is used to detect the domain specific ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi a visual word model to resolve synonym (visual heterogeneity) and polysemy problems, is proposed. The experimental results show that this approach can discover semantically meaningful visual content descriptions and recognise specific events, e.g., sports events, depicted in images efficiently. Since discovering the semantics of an image is an extremely challenging problem, one promising approach to enhance visual content interpretation is to use any associated textual information that accompanies an image, as a cue to predict the meaning of an image, by transforming this textual information into a structured annotation for an image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct types of information representation and modality, there are some strong, invariant, implicit, connections between images and any accompanying text information. Semantic analysis of image captions can be used by image retrieval systems to retrieve selected images more precisely. To do this, a Natural Language Processing (NLP) is exploited firstly in order to extract concepts from image captions. Next, an ontology-based knowledge model is deployed in order to resolve natural language ambiguities. To deal with the accompanying text information, two methods to extract knowledge from textual information have been proposed. First, metadata can be extracted automatically from text captions and restructured with respect to a semantic model. Second, the use of LSI in relation to a domain-specific ontology-based knowledge model enables the combined framework to tolerate ambiguities and variations (incompleteness) of metadata. The use of the ontology-based knowledge model allows the system to find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the proposed framework significantly enhances image retrieval and leads to narrowing of the semantic gap between lower level machinederived and higher level human-understandable conceptualisation

Discrimination-aware classification

Classifier construction is one of the most researched topics within the data mining and machine learning communities. Literally thousands of algorithms have been proposed. The quality of the learned models, however, depends critically on the quality of the training data. No matter which classifier inducer is applied, if the training data is incorrect, poor models will result. In this thesis, we study cases in which the input data is discriminatory and we are supposed to learn a classifier that optimizes accuracy, but does not discriminate in its predictions. Such situations occur naturally as artifacts of the data collection process when the training data is collected from different sources with different labeling criteria, when the data is generated by a biased decision process, or when the sensitive attribute, e.g., gender serves as a proxy for unobserved features. In many situations, a classifier that detects and uses the racial or gender discrimination is undesirable for legal reasons. The concept of discrimination is illustrated by the next example: Throughout the years, an employment bureau recorded various parameters of job candidates. Based on these parameters, the company wants to learn a model for partially automating the matchmaking between a job and a job candidate. A match is labeled as successful if the company hires the applicant. It turns out, however, that the historical data is biased; for higher board functions, Caucasian males are systematically being favored. A model learned directly on this data will learn this discriminatory behavior and apply it over future predictions. From an ethical and legal point of view it is of course unacceptable that a model discriminating in this way is deployed. Our proposed solutions to the discrimination problem fall into two broad categories. First, we propose pre-processing methods to remove the discrimination from the training dataset. Second, we propose solutions to the discrimination problem by directly pushing the non-discrimination constraints into classification models and post-processing of built models. We further studied the discrimination-aware classification paradigm in the presence of explanatory attributes that were correlated with the sensitive attribute, e.g., low income may be explained by the low education level. In such a case, as we show, not all discrimination can be considered bad. Therefore, we introduce a new way of measuring discrimination, by explicitly splitting it up into explainable and bad discrimination and propose methods to remove the bad discrimination only. We tried our discrimination-aware methods over real world data sets. We observed in our experiments that our methods show promising results and clearly outperform the traditional classification model w.r.t. accuracy discrimination trade-off. To conclude, we believe that discrimination-aware classification is a new and exciting area of research addressing a societally relevant problem

Discrimination-aware classification

Classifier construction is one of the most researched topics within the data mining and machine learning communities. Literally thousands of algorithms have been proposed. The quality of the learned models, however, depends critically on the quality of the training data. No matter which classifier inducer is applied, if the training data is incorrect, poor models will result. In this thesis, we study cases in which the input data is discriminatory and we are supposed to learn a classifier that optimizes accuracy, but does not discriminate in its predictions. Such situations occur naturally as artifacts of the data collection process when the training data is collected from different sources with different labeling criteria, when the data is generated by a biased decision process, or when the sensitive attribute, e.g., gender serves as a proxy for unobserved features. In many situations, a classifier that detects and uses the racial or gender discrimination is undesirable for legal reasons. The concept of discrimination is illustrated by the next example: Throughout the years, an employment bureau recorded various parameters of job candidates. Based on these parameters, the company wants to learn a model for partially automating the matchmaking between a job and a job candidate. A match is labeled as successful if the company hires the applicant. It turns out, however, that the historical data is biased; for higher board functions, Caucasian males are systematically being favored. A model learned directly on this data will learn this discriminatory behavior and apply it over future predictions. From an ethical and legal point of view it is of course unacceptable that a model discriminating in this way is deployed. Our proposed solutions to the discrimination problem fall into two broad categories. First, we propose pre-processing methods to remove the discrimination from the training dataset. Second, we propose solutions to the discrimination problem by directly pushing the non-discrimination constraints into classification models and post-processing of built models. We further studied the discrimination-aware classification paradigm in the presence of explanatory attributes that were correlated with the sensitive attribute, e.g., low income may be explained by the low education level. In such a case, as we show, not all discrimination can be considered bad. Therefore, we introduce a new way of measuring discrimination, by explicitly splitting it up into explainable and bad discrimination and propose methods to remove the bad discrimination only. We tried our discrimination-aware methods over real world data sets. We observed in our experiments that our methods show promising results and clearly outperform the traditional classification model w.r.t. accuracy discrimination trade-off. To conclude, we believe that discrimination-aware classification is a new and exciting area of research addressing a societally relevant problem

Machine-Learning Analysis of Serum Proteomics in Neuropathic Pain after Nerve Injury in Breast Cancer Surgery Points at Chemokine Signaling via SIRT2 Regulation

Background: Persistent postsurgical neuropathic pain (PPSNP) can occur after intraoperative damage to somatosensory nerves, with a prevalence of 29–57% in breast cancer surgery. Proteomics is an active research field in neuropathic pain and the first results support its utility for establishing diagnoses or finding therapy strategies. Methods: 57 women (30 non-PPSNP/27 PPSNP) who had experienced a surgeon-verified intercostobrachial nerve injury during breast cancer surgery, were examined for patterns in 74 serum proteomic markers that allowed discrimination between subgroups with or without PPSNP. Serum samples were obtained both before and after surgery. Results: Unsupervised data analyses, including principal component analysis and self-organizing maps of artificial neurons, revealed patterns that supported a data structure consistent with pain-related subgroup (non-PPSPN vs. PPSNP) separation. Subsequent supervised machine learning-based analyses revealed 19 proteins (CD244, SIRT2, CCL28, CXCL9, CCL20, CCL3, IL.10RA, MCP.1, TRAIL, CCL25, IL10, uPA, CCL4, DNER, STAMPB, CCL23, CST5, CCL11, FGF.23) that were informative for subgroup separation. In cross-validated training and testing of six different machine-learned algorithms, subgroup assignment was significantly better than chance, whereas this was not possible when training the algorithms with randomly permuted data or with the protein markers not selected. In particular, sirtuin 2 emerged as a key protein, presenting both before and after breast cancer treatments in the PPSNP compared with the non-PPSNP subgroup. Conclusions: The identified proteins play important roles in immune processes such as cell migration, chemotaxis, and cytokine-signaling. They also have considerable overlap with currently known targets of approved or investigational drugs. Taken together, several lines of unsupervised and supervised analyses pointed to structures in serum proteomics data, obtained before and after breast cancer surgery, that relate to neuroinflammatory processes associated with the development of neuropathic pain after an intraoperative nerve lesion

Machine-Learning Analysis of Serum Proteomics in Neuropathic Pain after Nerve Injury in Breast Cancer Surgery Points at Chemokine Signaling via SIRT2 Regulation

Background: Persistent postsurgical neuropathic pain (PPSNP) can occur after intraoperative damage to somatosensory nerves, with a prevalence of 29–57% in breast cancer surgery. Proteomics is an active research field in neuropathic pain and the first results support its utility for establishing diagnoses or finding therapy strategies. Methods: 57 women (30 non-PPSNP/27 PPSNP) who had experienced a surgeon-verified intercostobrachial nerve injury during breast cancer surgery, were examined for patterns in 74 serum proteomic markers that allowed discrimination between subgroups with or without PPSNP. Serum samples were obtained both before and after surgery. Results: Unsupervised data analyses, including principal component analysis and self-organizing maps of artificial neurons, revealed patterns that supported a data structure consistent with pain-related subgroup (non-PPSPN vs. PPSNP) separation. Subsequent supervised machine learning-based analyses revealed 19 proteins (CD244, SIRT2, CCL28, CXCL9, CCL20, CCL3, IL.10RA, MCP.1, TRAIL, CCL25, IL10, uPA, CCL4, DNER, STAMPB, CCL23, CST5, CCL11, FGF.23) that were informative for subgroup separation. In cross-validated training and testing of six different machine-learned algorithms, subgroup assignment was significantly better than chance, whereas this was not possible when training the algorithms with randomly permuted data or with the protein markers not selected. In particular, sirtuin 2 emerged as a key protein, presenting both before and after breast cancer treatments in the PPSNP compared with the non-PPSNP subgroup. Conclusions: The identified proteins play important roles in immune processes such as cell migration, chemotaxis, and cytokine-signaling. They also have considerable overlap with currently known targets of approved or investigational drugs. Taken together, several lines of unsupervised and supervised analyses pointed to structures in serum proteomics data, obtained before and after breast cancer surgery, that relate to neuroinflammatory processes associated with the development of neuropathic pain after an intraoperative nerve lesion

A hierarchal framework for recognising activities of daily life

PhDIn today’s working world the elderly who are dependent can sometimes be neglected by society. Statistically, after toddlers it is the elderly who are observed to have higher accident rates while performing everyday activities. Alzheimer’s disease is one of the major impairments that elderly people suffer from, and leads to the elderly person not being able to live an independent life due to forgetfulness. One way to support elderly people who aspire to live an independent life and remain safe in their home is to find out what activities the elderly person is carrying out at a given time and provide appropriate assistance or institute safeguards. The aim of this research is to create improved methods to identify tasks related to activities of daily life and determine a person’s current intentions and so reason about that person’s future intentions. A novel hierarchal framework has been developed, which recognises sensor events and maps them to significant activities and intentions. As privacy is becoming a growing concern, the monitoring of an individual’s behaviour can be seen as intrusive. Hence, the monitoring is based around using simple non intrusive sensors and tags on everyday objects that are used to perform daily activities around the home. Specifically there is no use of any cameras or visual surveillance equipment, though the techniques developed are still relevant in such a situation. Models for task recognition and plan recognition have been developed and tested on scenarios where the plans can be interwoven. Potential targets are people in the first stages of Alzheimer’s disease and in the structuring of the library of kernel plan sequences, typical routines used to sustain meaningful activity have been used. Evaluations have been carried out using volunteers conducting activities of daily life in an experimental home environment. The results generated from the sensors have been interpreted and analysis of developed algorithms has been made. The outcomes and findings of these experiments demonstrate that the developed hierarchal framework is capable of carrying activity recognition as well as being able to carry out intention analysis, e.g. predicting what activity they are most likely to carry out next

A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms

Machine Learning Approaches for Natural Resource Data

Abstract Real life applications involving efficient management of natural resources are dependent on accurate geographical information. This information is usually obtained by manual on-site data collection, via automatic remote sensing methods, or by the mixture of the two. Natural resource management, besides accurate data collection, also requires detailed analysis of this data, which in the era of data flood can be a cumbersome process. With the rising trend in both computational power and storage capacity, together with lowering hardware prices, data-driven decision analysis has an ever greater role. In this thesis, we examine the predictability of terrain trafficability conditions and forest attributes by using a machine learning approach with geographic information system data. Quantitative measures on the prediction performance of terrain conditions using natural resource data sets are given through five distinct research areas located around Finland. Furthermore, the estimation capability of key forest attributes is inspected with a multitude of modeling and feature selection techniques. The research results provide empirical evidence on whether the used natural resource data is sufficiently accurate enough for practical applications, or if further refinement on the data is needed. The results are important especially to forest industry since even slight improvements to the natural resource data sets utilized in practice can result in high saves in terms of operation time and costs. Model evaluation is also addressed in this thesis by proposing a novel method for estimating the prediction performance of spatial models. Classical model goodness of fit measures usually rely on the assumption of independently and identically distributed data samples, a characteristic which normally is not true in the case of spatial data sets. Spatio-temporal data sets contain an intrinsic property called spatial autocorrelation, which is partly responsible for breaking these assumptions. The proposed cross validation based evaluation method provides model performance estimation where optimistic bias due to spatial autocorrelation is decreased by partitioning the data sets in a suitable way. Keywords: Open natural resource data, machine learning, model evaluationTiivistelmä Käytännön sovellukset, joihin sisältyy luonnonvarojen hallintaa ovat riippuvaisia tarkasta paikkatietoaineistosta. Tämä paikkatietoaineisto kerätään usein manuaalisesti paikan päällä, automaattisilla kaukokartoitusmenetelmillä tai kahden edellisen yhdistelmällä. Luonnonvarojen hallinta vaatii tarkan aineiston keräämisen lisäksi myös sen yksityiskohtaisen analysoinnin, joka tietotulvan aikakautena voi olla vaativa prosessi. Nousevan laskentatehon, tallennustilan sekä alenevien laitteistohintojen myötä datapohjainen päätöksenteko on yhä suuremmassa roolissa. Tämä väitöskirja tutkii maaston kuljettavuuden ja metsäpiirteiden ennustettavuutta käyttäen koneoppimismenetelmiä paikkatietoaineistojen kanssa. Maaston kuljettavuuden ennustamista mitataan kvantitatiivisesti käyttäen kaukokartoitusaineistoa viideltä eri tutkimusalueelta ympäri Suomea. Tarkastelemme lisäksi tärkeimpien metsäpiirteiden ennustettavuutta monilla eri mallintamistekniikoilla ja piirteiden valinnalla. Väitöstyön tulokset tarjoavat empiiristä todistusaineistoa siitä, onko käytetty luonnonvaraaineisto riittävän laadukas käytettäväksi käytännön sovelluksissa vai ei. Tutkimustulokset ovat tärkeitä erityisesti metsäteollisuudelle, koska pienetkin parannukset luonnonvara-aineistoihin käytännön sovelluksissa voivat johtaa suuriin säästöihin niin operaatioiden ajankäyttöön kuin kuluihin. Tässä työssä otetaan kantaa myös mallin evaluointiin esittämällä uuden menetelmän spatiaalisten mallien ennustuskyvyn estimointiin. Klassiset mallinvalintakriteerit nojaavat yleensä riippumattomien ja identtisesti jakautuneiden datanäytteiden oletukseen, joka ei useimmiten pidä paikkaansa spatiaalisilla datajoukoilla. Spatio-temporaaliset datajoukot sisältävät luontaisen ominaisuuden, jota kutsutaan spatiaaliseksi autokorrelaatioksi. Tämä ominaisuus on osittain vastuussa näiden oletusten rikkomisesta. Esitetty ristiinvalidointiin perustuva evaluointimenetelmä tarjoaa mallin ennustuskyvyn mitan, missä spatiaalisen autokorrelaation vaikutusta vähennetään jakamalla datajoukot sopivalla tavalla. Avainsanat: Avoin luonnonvara-aineisto, koneoppiminen, mallin evaluoint