Content-Based Image Retrieval for Computer Tomography Images Using Wavelet Descriptors

An approach to building a CBIR-system for searching computer tomography images using the methods of wavelet-analysis is presented in this work. The index vectors are constructed on the basis of the local features of the image and on their positions. The purpose of the proposed system is to extract visually similar data from the individual personal records and from analogous analysis of other patients.

Searching and retrieving in content-based repositories of formal mathematical knowledge

In this thesis, the author presents a query language for an RDF (Resource Description Framework) database and discusses its applications in the context of the HELM project (the Hypertextual Electronic Library of Mathematics). This language aims at meeting the main requirements coming from the RDF community. in particular it includes: a human readable textual syntax and a machine-processable XML (Extensible Markup Language) syntax both for queries and for query results, a rigorously exposed formal semantics, a graph-oriented RDF data access model capable of exploring an entire RDF graph (including both RDF Models and RDF Schemata), a full set of Boolean operators to compose the query constraints, fully customizable and highly structured query results having a 4-dimensional geometry, some constructions taken from ordinary programming languages that simplify the formulation of complex queries. The HELM project aims at integrating the modern tools for the automation of formal reasoning with the most recent electronic publishing technologies, in order create and maintain a hypertextual, distributed virtual library of formal mathematical knowledge. In the spirit of the Semantic Web, the documents of this library include RDF metadata describing their structure and content in a machine-understandable form. Using the author's query engine, HELM exploits this information to implement some functionalities allowing the interactive and automatic retrieval of documents on the basis of content-aware requests that take into account the mathematical nature of these documents.

ICICLE: A semantic-based retrieval system for WWW images

In this paper, we present ICICLE (Image ChainNet and Incremental Clustering Engine), a prototype system that we have developed to efficiently and effectively retrieve WWW images based on image semantics. ICICLE has two distinguishing features. First, it employs a novel image representation model called Weight ChainNet to capture the semantics of the image content. A new formula, called list space model, for computing semantic similarities is also introduced. Second, to speed up retrieval, ICICLE employs an incremental clustering mechanism, ICC (Incremental Clustering on ChainNet), to cluster images with similar semantics into the same partition. Each cluster has a summary representative and all clusters' representatives are further summarized into a balanced and full binary tree structure. We conducted an extensive performance study to evaluate ICICLE. Compared with some recently proposed methods, our results show that ICICLE provides better recall and precision. Our clustering technique ICC facilitates speedy retrieval of images without sacrificing recall and precision significantly.

An Ontology- Content-based Filtering Method

Traditional content-based filtering methods usually utilize text extraction and classification techniques for building user profiles as well as for representations of contents, i.e. item profiles. These methods have some disadvantages e.g. mismatch between user profile terms and item profile terms, leading to low performance. Some of the disadvantages can be overcome by incorporating a common ontology which enables representing both the users' and the items' profiles with concepts taken from the same vocabulary. We propose a new content-based method for filtering and ranking the relevancy of items for users, which utilizes a hierarchical ontology. The method measures the similarity of the user's profile to the items' profiles, considering the existing of mutual concepts in the two profiles, as well as the existence of "related" concepts, according to their position in the ontology. The proposed filtering algorithm computes the similarity between the users' profiles and the items' profiles, and rank-orders the relevant items according to their relevancy to each user. The method is being implemented in ePaper, a personalized electronic newspaper project, utilizing a hierarchical ontology designed specifically for classification of News items. It can, however, be utilized in other domains and extended to other ontologies.

Distance Matrix Approach to Content Image Retrieval

As the volume of image data and the need of using it in various applications is growing significantly in the last days it brings a necessity of retrieval efficiency and effectiveness. Unfortunately, existing indexing methods are not applicable to a wide range of problem-oriented fields due to their operating time limitations and strong dependency on the traditional descriptors extracted from the image. To meet higher requirements, a novel distance-based indexing method for region-based image retrieval has been proposed and investigated. The method creates premises for considering embedded partitions of images to carry out the search with different refinement or roughening level and so to seek the image meaningful content.

A generalized multidimensional index structure for multimedia data to support content-based similarity searches in a collaborative search environment

Since multimedia data, such as images and videos, are way more expressive and informative than ordinary text-based data, people find it more attractive to communicate and express with them. Additionally, with the rising popularity of social networking tools such as Facebook and Twitter, multimedia information retrieval can no longer be considered a solitary task. Rather, people constantly collaborate with one another while searching and retrieving information. But the very cause of the popularity of multimedia data, the huge and different types of information a single data object can carry, makes their management a challenging task. Multimedia data is commonly represented as multidimensional feature vectors and carry high-level semantic information. These two characteristics make them very different from traditional alpha-numeric data. Thus, to try to manage them with frameworks and rationales designed for primitive alpha-numeric data, will be inefficient. An index structure is the backbone of any database management system. It has been seen that index structures present in existing relational database management frameworks cannot handle multimedia data effectively. Thus, in this dissertation, a generalized multidimensional index structure is proposed which accommodates the atypical multidimensional representation and the semantic information carried by different multimedia data seamlessly from within one single framework. Additionally, the dissertation investigates the evolving relationships among multimedia data in a collaborative environment and how such information can help to customize the design of the proposed index structure, when it is used to manage multimedia data in a shared environment. Extensive experiments were conducted to present the usability and better performance of the proposed framework over current state-of-art approaches.

How to integrate stories and ICT in content-based units of work for English learning

The aim of this article is to show how it is possible to integrate stories and ICT in Content Language Integrated Learning (CLIL) for English as a foreign language (EFL) learning in bilingual schools. Two Units of Work are presented. One, for the second year of Primary, is based on a Science topic, ‘Materials’. The story used is ‘The three little pigs’ and the computer program ‘JClic’. The other one is based on a Science and Arts topic for the sixth year of Primary, the story used is ‘Charlotte’s Web’ and the computer program ‘Atenex’.

Recommending media content based on machine learning methods

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Tuning metadata for better movie content-based recommendation systems

The increasing number of television channels, on-demand services and online content, is expected to contribute to a better quality of experience for a costumer of such a service. However, the lack of efficient methods for finding the right content, adapted to personal interests, may lead to a progressive loss of clients. In such a scenario, recommendation systems are seen as a tool that can fill this gap and contribute to the loyalty of users. Multimedia content, namely films and television programmes are usually described using a set of metadata elements that include the title, a genre, the date of production, and the list of directors and actors. This paper provides a deep study on how the use of different metadata elements can contribute to increase the quality of the recommendations suggested. The analysis is conducted using Netflix and Movielens datasets and aspects such as the granularity of the descriptions, the accuracy metric used and the sparsity of the data are taken into account. Comparisons with collaborative approaches are also presented.

Yield of essential oil and safrole content based on fresh and dry biomass of long pepper in the Brazilian Amazon

Long pepper (Piper hispidinervum) is an Amazonian species of commercial interest due to the production of safrole. Drying long pepper biomass to extract safrole is a time consuming and costly process that can also result in the contamination of the material by microorganisms. The objective of this study was to analyze the yield of essential oil and safrole content of fresh and dried biomass of long pepper accessions maintained in the Active Germoplasm Bank of Embrapa Acre, in the state of Acre, Brazil, aiming at selecting genotypes with best performance on fresh biomass to recommend to the breeding program of the species. Yield of essential oil and safrole content were assessed in 15 long pepper accessions. The essential oil extraction was performed by hydrodistillation and analyzed by gas chromatography. A joint analysis of experiments was performed and the means of essential oil yield and safrole content for each biomass were compared by Student's t-test. There was variability in the essential oil yield and safrole content. There was no difference between the types of biomass for oil yield; however to the safrole content there was difference. Populations 9, 10, 12 and 15 had values of oil yield between 4.1 and 5.3%, and safrole content between 87.2 and 94.3%. The drying process does not interfere in oil productivity. These populations have potential for selection to the long pepper breeding program using oil extraction in the fresh biomass

CONSUMER PERCEPTIONS ON A CONTENT-BASED DIGITAL SERVICE. The usage and service quality of Masennusinfo.fi

Kuluttajat käyttävät sisältöpohjaisia digitaalisia palveluita jatkuvasti saadakseen lisää tietoa terveydestään. Samalla he arvioivat käyttämiensä palveluiden laatua. Jotta yritykset voisivat suunnitella ja tarjota parhaita mahdollisia digitaalisia palveluita kuluttajille, yritysten tulisi tunnistaa ja analysoida kuluttajien kokemuksia ja käyttötarkoituksia heidän palveluissaan. Tämän tutkimuksen tarkoituksena on kuvailla kuluttajien näkemyksiä Masennusinfo.fi:stä, joka on sisältöpohjainen digitaalinen palvelu, ja joka tarjoaa käyttäjilleen tietoa masennuksesta. Päämääränä on selvittää, kuinka kuluttajat kokevat lääkeyrityksen tarjoaman palvelun laadun ja mihin tarkoituksiin sitä käytetään. Tutkimuksen tarkoitus voidaan jakaa kolmeen osa-ongelmaan: • Mihin tarkoituksiin kuluttajat käyttävät sisältöpohjaisia digitaalisia palveluita? • Miten kuluttajat kokevat näiden palveluiden laadun? • Kuinka käyttötarkoitus ja koettu laatu eroavat eri käyttäjäryhmissä? Tutkimus toteutetaan web-pohjaisella kyselytutkimuksella. Mittarit tehdään teoreettisen viitekehyksen pohjalta, joka perustuu aikaisempaan tutkimukseen. Tutkimuksen empiirinen osuus suoritetaan pop-up tutkimuksella, joka sijoitetaan tutkittavalle sivustolle antaen näin kaikille palvelun käyttäjille mahdollisuuden vastata kyselyyn. Tulokset osoittavat, että palvelua käyttävät suurimmaksi osaksi naiset, suhteellisen nuoret 16−29-vuotiaat, tai yli keski-ikäiset 50−65-vuotiaat henkilöt, jotka ovat joko työssäkäyviä tai opiskelijoita ja korkeasti koulutettuja. Masennusinfo.fi nähdään laadukkaana palveluna kaikissa käyttäjäryhmissä sekä sen käytettävyyden että sisällön perusteella. Käyttötarkoituksetkin ovat jokseenkin samankaltaisia eri käyttäjäryhmissä. Yleensä palvelua käytetään tiedon hakemiseen sairauden alkuvaiheessa. Löydöksien perusteella esitetään, että palvelua muokataan vastaamaan yhä paremmin sen käyttötarkoituksia ja tyypillistä käyttäjäprofiilia. Koska muutamia pieniä eroja käyttäjäryhmien näkemyksissä havaittiin, palveluiden tuottaja päättää, minkä ryhmän mieltymyksiä se noudattaa.

Consumer perceptions on a content-based digital service:the usage and service quality of Masennusinfo.fi

Kuluttajat käyttävät sisältöpohjaisia digitaalisia palveluita jatkuvasti saadakseen lisää tietoa terveydestään. Samalla he arvioivat käyttämiensä palveluiden laatua. Jotta yritykset voisivat suunnitella ja tarjota parhaita mahdollisia digitaalisia palveluita kuluttajille, yritysten tulisi tunnistaa ja analysoida kuluttajien kokemuksia ja käyttötarkoituksia heidän palveluissaan. Tämän tutkimuksen tarkoituksena on kuvailla kuluttajien näkemyksiä Masennusinfo.fi:stä, joka on sisältöpohjainen digitaalinen palvelu, ja joka tarjoaa käyttäjilleen tietoa masennuksesta. Päämääränä on selvittää, kuinka kuluttajat kokevat lääkeyrityksen tarjoaman palvelun laadun ja mihin tarkoituksiin sitä käytetään. Tutkimuksen tarkoitus voidaan jakaa kolmeen osa-ongelmaan: Mihin tarkoituksiin kuluttajat käyttävät sisältöpohjaisia digitaalisia palveluita? Miten kuluttajat kokevat näiden palveluiden laadun? Kuinka käyttötarkoitus ja koettu laatu eroavat eri käyttäjäryhmissä? Tutkimus toteutetaan web-pohjaisella kyselytutkimuksella. Mittarit tehdään teoreettisen viitekehyksen pohjalta, joka perustuu aikaisempaan tutkimukseen. Tutkimuksen empiirinen osuus suoritetaan pop-up tutkimuksella, joka sijoitetaan tutkittavalle sivustolle antaen näin kaikille palvelun käyttäjille mahdollisuuden vastata kyselyyn. Tulokset osoittavat, että palvelua käyttävät suurimmaksi osaksi naiset, suhteellisen nuoret 16−29- vuotiaat, tai yli keski-ikäiset 50−65-vuotiaat henkilöt, jotka ovat joko työssäkäyviä tai opiskelijoita ja korkeasti koulutettuja. Masennusinfo.fi nähdään laadukkaana palveluna kaikissa käyttäjäryhmissä sekä sen käytettävyyden että sisällön perusteella. Käyttötarkoituksetkin ovat jokseenkin samankaltaisia eri käyttäjäryhmissä. Yleensä palvelua käytetään tiedon hakemiseen sairauden alkuvaiheessa. Löydöksien perusteella esitetään, että palvelua muokataan vastaamaan yhä paremmin sen käyttötarkoituksia ja tyypillistä käyttäjäprofiilia. Koska muutamia pieniä eroja käyttäjäryhmien näkemyksissä havaittiin, palveluiden tuottaja päättää, minkä ryhmän mieltymyksiä se noudattaa.

English-Malayalam Cross-Lingual Information Retrieval – an Experience

This paper describes about an English-Malayalam Cross-Lingual Information Retrieval system. The system retrieves Malayalam documents in response to query given in English or Malayalam. Thus monolingual information retrieval is also supported in this system. Malayalam is one of the most prominent regional languages of Indian subcontinent. It is spoken by more than 37 million people and is the native language of Kerala state in India. Since we neither had any full-fledged online bilingual dictionary nor any parallel corpora to build the statistical lexicon, we used a bilingual dictionary developed in house for translation. Other language specific resources like Malayalam stemmer, Malayalam morphological root analyzer etc developed in house were used in this work