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

Compression-based Image Registration

Image registration is an important component of image analysis used to align two or more images. In this paper, we present a new framework for image registration based on compression. The basic idea underlying our approach is the conjecture that two images are correctly registered when we can maximally compress one image given the information in the other. The contribution of this paper is twofold. First, we show that the image registration process can be dealt with from the perspective of a compression problem. Second, we demonstrate that the similarity metric, introduced by Li et al., performs well in image registration. Two different versions of the similarity metric have been used: the Kolmogorov version, computed using standard real-world compressors, and the Shannon version, calculated from an estimation of the entropy rate of the images

Multiresolution-based image fusion with additive wavelet decomposition

The standard data fusion methods may not be satisfactory to merge a high-resolution panchromatic image and a low-resolution multispectral image because they can distort the spectral characteristics of the multispectral data. The authors developed a technique, based on multiresolution wavelet decomposition, for the merging and data fusion of such images. The method presented consists of adding the wavelet coefficients of the high-resolution image to the multispectral (low-resolution) data. They have studied several possibilities concluding that the method which produces the best results consists in adding the high order coefficients of the wavelet transform of the panchromatic image to the intensity component (defined as L=(R+G+B)/3) of the multispectral image. The method is, thus, an improvement on standard intensity-hue-saturation (IHS or LHS) mergers. They used the ¿a trous¿ algorithm which allows the use of a dyadic wavelet to merge nondyadic data in a simple and efficient scheme. They used the method to merge SPOT and LANDSATTM images. The technique presented is clearly better than the IHS and LHS mergers in preserving both spectral and spatial information.

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.

Development of 2-D MRT based Image Compression Techniques

The thesis explores the area of still image compression. The image compression techniques can be broadly classified into lossless and lossy compression. The most common lossy compression techniques are based on Transform coding, Vector Quantization and Fractals. Transform coding is the simplest of the above and generally employs reversible transforms like, DCT, DWT, etc. Mapped Real Transform (MRT) is an evolving integer transform, based on real additions alone. The present research work aims at developing new image compression techniques based on MRT. Most of the transform coding techniques employ fixed block size image segmentation, usually 8×8. Hence, a fixed block size transform coding is implemented using MRT and the merits and demerits are analyzed for both 8×8 and 4×4 blocks. The N2 unique MRT coefficients, for each block, are computed using templates. Considering the merits and demerits of fixed block size transform coding techniques, a hybrid form of these techniques is implemented to improve the performance of compression. The performance of the hybrid coder is found to be better compared to the fixed block size coders. Thus, if the block size is made adaptive, the performance can be further improved. In adaptive block size coding, the block size may vary from the size of the image to 2×2. Hence, the computation of MRT using templates is impractical due to memory requirements. So, an adaptive transform coder based on Unique MRT (UMRT), a compact form of MRT, is implemented to get better performance in terms of PSNR and HVS The suitability of MRT in vector quantization of images is then experimented. The UMRT based Classified Vector Quantization (CVQ) is implemented subsequently. The edges in the images are identified and classified by employing a UMRT based criteria. Based on the above experiments, a new technique named “MRT based Adaptive Transform Coder with Classified Vector Quantization (MATC-CVQ)”is developed. Its performance is evaluated and compared against existing techniques. A comparison with standard JPEG & the well-known Shapiro’s Embedded Zero-tree Wavelet (EZW) is done and found that the proposed technique gives better performance for majority of images

Example Based Image Analysis and Synthesis

Image analysis and graphics synthesis can be achieved with learning techniques using directly image examples without physically-based, 3D models. In our technique: -- the mapping from novel images to a vector of "pose" and "expression" parameters can be learned from a small set of example images using a function approximation technique that we call an analysis network; -- the inverse mapping from input "pose" and "expression" parameters to output images can be synthesized from a small set of example images and used to produce new images using a similar synthesis network. The techniques described here have several applications in computer graphics, special effects, interactive multimedia and very low bandwidth teleconferencing.

Compression-based Image Registration

Image registration is an important component of image analysis used to align two or more images. In this paper, we present a new framework for image registration based on compression. The basic idea underlying our approach is the conjecture that two images are correctly registered when we can maximally compress one image given the information in the other. The contribution of this paper is twofold. First, we show that the image registration process can be dealt with from the perspective of a compression problem. Second, we demonstrate that the similarity metric, introduced by Li et al., performs well in image registration. Two different versions of the similarity metric have been used: the Kolmogorov version, computed using standard real-world compressors, and the Shannon version, calculated from an estimation of the entropy rate of the images

Image retrieval techniques for THz applications in cultural heritage

Techniques to retrieve reliable images from complicated objects are described, overcoming problems introduced by uneven surfaces, giving enhanced depth resolution and improving image contrast. The techniques are illustrated with application to THz imaging of concealed wall paintings.

Combining collaborative and content-based filtering to recommend research papers

The number of research papers available today is growing at a staggering rate, generating a huge amount of information that people cannot keep up with. According to a tendency indicated by the United States’ National Science Foundation, more than 10 million new papers will be published in the next 20 years. Because most of these papers will be available on the Web, this research focus on exploring issues on recommending research papers to users, in order to directly lead users to papers of their interest. Recommender systems are used to recommend items to users among a huge stream of available items, according to users’ interests. This research focuses on the two most prevalent techniques to date, namely Content-Based Filtering and Collaborative Filtering. The first explores the text of the paper itself, recommending items similar in content to the ones the user has rated in the past. The second explores the citation web existing among papers. As these two techniques have complementary advantages, we explored hybrid approaches to recommending research papers. We created standalone and hybrid versions of algorithms and evaluated them through both offline experiments on a database of 102,295 papers, and an online experiment with 110 users. Our results show that the two techniques can be successfully combined to recommend papers. The coverage is also increased at the level of 100% in the hybrid algorithms. In addition, we found that different algorithms are more suitable for recommending different kinds of papers. Finally, we verified that users’ research experience influences the way users perceive recommendations. In parallel, we found that there are no significant differences in recommending papers for users from different countries. However, our results showed that users’ interacting with a research paper Recommender Systems are much happier when the interface is presented in the user’s native language, regardless the language that the papers are written. Therefore, an interface should be tailored to the user’s mother language.

JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.