Semantics and statistics for automated image annotation

Automated image annotation consists of a number of techniques that aim to find the correlation between words and image features such as colour, shape, and texture to provide correct annotation words to images. In particular, approaches based on Bayesian theory use machine-learning techniques to learn statistical models from a training set of pre-annotated images and apply them to generate annotations for unseen images. The focus of this thesis lies in demonstrating that an approach, which goes beyond learning the statistical correlation between words and visual features and also exploits information about the actual semantics of the words used in the annotation process, is able to improve the performance of probabilistic annotation systems. Specifically, I present three experiments. Firstly, I introduce a novel approach that automatically refines the annotation words generated by a non-parametric density estimation model using semantic relatedness measures. Initially, I consider semantic measures based on co-occurrence of words in the training set. However, this approach can exhibit limitations, as its performance depends on the quality and coverage provided by the training data. For this reason, I devise an alternative solution that combines semantic measures based on knowledge sources, such as WordNet and Wikipedia, with word co-occurrence in the training set and on the web, to achieve statistically significant results over the baseline. Secondly, I investigate the effect of using semantic measures inside an evaluation measure that computes the performance of an automated image annotation system, whose annotation words adopt the hierarchical structure of an ontology. This is the case of the ImageCLEF2009 collection. Finally, I propose a Markov Random Field that exploits the semantic context dependencies of the image. The best result obtains a mean average precision of 0.32, which is consistent with the state-of-the-art in automated image annotation for the Corel 5k dataset. </br

Shangri-La: a medical case-based retrieval tool

Large amounts of medical visual data are produced in hospitals daily and made available continuously via publications in the scientific literature, representing the medical knowledge. However, it is not always easy to find the desired information and in clinical routine the time to fulfil an information need is often very limited. Information retrieval systems are a useful tool to provide access to these documents/images in the biomedical literature related to information needs of medical professionals. Shangri–La is a medical retrieval system that can potentially help clinicians to make decisions on difficult cases. It retrieves articles from the biomedical literature when querying a case description and attached images. The system is based on a multimodal retrieval approach with a focus on the integration of visual information connected to text. The approach includes a query–adaptive multimodal fusion criterion that analyses if visual features are suitable to be fused with text for the retrieval. Furthermore, image modality information is integrated in the retrieval step. The approach is evaluated using the ImageCLEFmed 2013 medical retrieval benchmark and can thus be compared to other approaches. Results show that the final approach outperforms the best multimodal approach submitted to ImageCLEFmed 2013

Use Case Oriented Medical Visual Information Retrieval & System Evaluation

Large amounts of medical visual data are produced daily in hospitals, while new imaging techniques continue to emerge. In addition, many images are made available continuously via publications in the scientific literature and can also be valuable for clinical routine, research and education. Information retrieval systems are useful tools to provide access to the biomedical literature and fulfil the information needs of medical professionals. The tools developed in this thesis can potentially help clinicians make decisions about difficult diagnoses via a case-based retrieval system based on a use case associated with a specific evaluation task. This system retrieves articles from the biomedical literature when querying with a case description and attached images. This thesis proposes a multimodal approach for medical case-based retrieval with focus on the integration of visual information connected to text. Furthermore, the ImageCLEFmed evaluation campaign was organised during this thesis promoting medical retrieval system evaluation

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Biomedical information extraction for matching patients to clinical trials

Digital Medical information had an astonishing growth on the last decades, driven by an unprecedented number of medical writers, which lead to a complete revolution in what and how much information is available to the health professionals. The problem with this wave of information is that performing a precise selection of the information retrieved by medical information repositories is very exhaustive and time consuming for physicians. This is one of the biggest challenges for physicians with the new digital era: how to reduce the time spent finding the perfect matching document for a patient (e.g. intervention articles, clinical trial, prescriptions). Precision Medicine (PM) 2017 is the track by the Text REtrieval Conference (TREC), that is focused on this type of challenges exclusively for oncology. Using a dataset with a large amount of clinical trials, this track is a good real life example on how information retrieval solutions can be used to solve this types of problems. This track can be a very good starting point for applying information extraction and retrieval methods, in a very complex domain. The purpose of this thesis is to improve a system designed by the NovaSearch team for TREC PM 2017 Clinical Trials task, which got ranked on the top-5 systems of 2017. The NovaSearch team also participated on the 2018 track and got a 15% increase on precision compared to the 2017 one. It was used multiple IR techniques for information extraction and processing of data, including rank fusion, query expansion (e.g. Pseudo relevance feedback, Mesh terms expansion) and experiments with Learning to Rank (LETOR) algorithms. Our goal is to retrieve the best possible set of trials for a given patient, using precise documents filters to exclude the unwanted clinical trials. This work can open doors in what can be done for searching and perceiving the criteria to exclude or include the trials, helping physicians even on the more complex and difficult information retrieval tasks

A ranking framework and evaluation for diversity-based retrieval

There has been growing momentum in building information retrieval (IR) systems that consider both relevance and diversity of retrieved information, which together improve the usefulness of search results as perceived by users. Some users may genuinely require a set of multiple results to satisfy their information need as there is no single result that completely fulfils the need. Others may be uncertain about their information need and they may submit ambiguous or broad (faceted) queries, either intentionally or unintentionally. A sensible approach to tackle these problems is to diversify search results to address all possible senses underlying those queries or all possible answers satisfying the information need. In this thesis, we explore three aspects of diversity-based document retrieval: 1) recommender systems, 2) retrieval algorithms, and 3) evaluation measures. This first goal of this thesis is to provide an understanding of the need for diversity in search results from the users’ perspective. We develop an interactive recommender system for the purpose of a user study. Designed to facilitate users engaged in exploratory search, the system is featured with content-based browsing, aspectual interfaces, and diverse recommendations. While the diverse recommendations allow users to discover more and different aspects of a search topic, the aspectual interfaces allow users to manage and structure their own search process and results regarding aspects found during browsing. The recommendation feature mines implicit relevance feedback information extracted from a user’s browsing trails and diversifies recommended results with respect to document contents. The result of our user-centred experiment shows that result diversity is needed in realistic retrieval scenarios. Next, we propose a new ranking framework for promoting diversity in a ranked list. We combine two distinct result diversification patterns; this leads to a general framework that enables the development of a variety of ranking algorithms for diversifying documents. To validate our proposal and to gain more insights into approaches for diversifying documents, we empirically compare our integration framework against a common ranking approach (i.e. the probability ranking principle) as well as several diversity-based ranking strategies. These include maximal marginal relevance, modern portfolio theory, and sub-topic-aware diversification based on sub-topic modelling techniques, e.g. clustering, latent Dirichlet allocation, and probabilistic latent semantic analysis. Our findings show that the two diversification patterns can be employed together to improve the effectiveness of ranking diversification. Furthermore, we find that the effectiveness of our framework mainly depends on the effectiveness of the underlying sub-topic modelling techniques. Finally, we examine evaluation measures for diversity retrieval. We analytically identify an issue affecting the de-facto standard measure, novelty-biased discounted cumulative gain (α-nDCG). This issue prevents the measure from behaving as desired, i.e. assessing the effectiveness of systems that provide complete coverage of sub-topics by avoiding excessive redundancy. We show that this issue is of importance as it highly affects the evaluation of retrieval systems, specifically by overrating top-ranked systems that repeatedly retrieve redundant information. To overcome this issue, we derive a theoretically sound solution by defining a safe threshold on a query-basis. We examine the impact of arbitrary settings of the α-nDCG parameter. We evaluate the intuitiveness and reliability of α-nDCG when using our proposed setting on both real and synthetic rankings. We demonstrate that the diversity of document rankings can be intuitively measured by employing the safe threshold. Moreover, our proposal does not harm, but instead increases the reliability of the measure in terms of discriminative power, stability, and sensitivity.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Mid-level Image Representation for Fruit Fly Identification (Diptera: Tephritidae)

Fruit flies are of huge biological and economic importance for the farming of different countries in the World, especially for Brazil. Brazil is the third largest fruit producer in the world with 44 million tons in 2016. The direct and indirect losses caused by fruit flies can exceed USD 2 billion, putting these pests as one of the biggest problems of the world agriculture. In Brazil, it is estimated that the economic losses directly related to production, the cost of pest control and in the loss of export markets, are between USD 120 and 200 million/year. The species of the genus Anastrepha are among the fruit flies economically important in the America tropics and subtropics with approximately 300 known species, of which 120 are recorded in Brazil. However, few species are economically important in Brazil and are considered pests of quarantine significance by regulatory agencies. In this sense, the development of automatic and semi-automatic tools for fruit fly species identification of the genus Anastrepha can assist the few existing specialists to reduce the insect analysis time and the economic losses related to these agricultural pests. We propose to apply mid-level image representations based on local descriptors for fruit fly identification tasks of three species of the genus Anastrepha. In our experiments, several local image descriptors based on keypoints and machine learning techniques have been studied for the target task. Furthermore, the proposed approaches have achieved excellent effectiveness results when compared with a state-of-art technique.CNPq Universal ProjectFAPESPCNPqCAPESUniv Fed Sao Paulo, Inst Sci & Technol, GIBIS Lab, Sao Jose Dos Campos, BrazilUniv Estadual Campinas, Inst Comp, RECOD Lab, Campinas, SP, BrazilUniv Sao Paulo, Luiz de Queiroz Coll Agr, Piracicaba, BrazilUniv Fed Sao Paulo, Inst Sci & Technol, GIBIS Lab, Sao Jose Dos Campos, BrazilCNPq Universal Project: 408919/2016-7Web of Scienc

Triagem robusta de melanoma : em defesa dos descritores aprimorados de nível médio

Orientadores: Eduardo Alves do Valle Junior, Sandra Eliza Fontes de AvilaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Melanoma é o tipo de câncer de pele que mais leva à morte, mesmo sendo o mais curável, se detectado precocemente. Considerando que a presença de um dermatologista em tempo integral não é economicamente viável para muitas cidades e especialmente em comunidades carentes, ferramentas de auxílio ao diagnóstico para a triagem do melanoma têm sido um tópico de pesquisa ativo. Muitos trabalhos existentes são baseados no modelo Bag-of-Visual-Words (BoVW), combinando descritores de cor e textura. No entanto, o modelo BoVW vem se aprimorando e hoje existem várias extensões que levam a melhores taxas de acerto em tarefas gerais de classificação de imagens. Estes modelos avançados ainda não foram explorados para rastreio de melanoma, motivando assim este trabalho. Aqui nós apresentamos uma nova abordagem para rastreio de melanoma baseado nos descritores BossaNova, que são estado-da-arte, mostrando resultados muito promissores, com uma AUC de 93,7%. Este trabalho também propõe uma nova estratégia de pooling espacial especialmente desenhada para rastreio de melanoma. Outra contribuição dessa pesquisa é o uso inédito do BossaNova na classificação de melanoma. Isso abre oportunidades de exploração deste descritor em outros contextos médicosAbstract: Melanoma is the type of skin cancer that most leads to death, even being the most curable, if detected early. Since the presence of a full time dermatologist is not economical feasible for many small cities and specially in underserved communities, computer-aided diagnosis for melanoma screening has been a topic of active research. Much of the existing art is based on the Bag-of-Visual-Words (BoVW) model, combining color and texture descriptors. However, the BoVW model has been improving and nowadays there are several extensions that perform better classification rates in general image classification tasks. These enhanced models were not explored yet for melanoma screening, thus motivating our work. Here we present a new approach for melanoma screening, based upon the state-of-the-art BossaNova descriptors, showing very promising results for screening, reaching an AUC of up to 93.7%. This work also proposes a new spatial pooling strategy specially designed for melanoma screening. Other contribution of this research is the unprecedented use of BossaNova in melanoma classification. This opens the opportunity to explore this enhanced mid-level descriptors in other medical contextsMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric