Affective image classification

Mädchenname: Banova, JanaZsfassung in dt. SpracheBilder sprechen mehr als tausend Worte. Einer ihrer Aspekte ist, dass sie auf Menschen eine emotionale Wirkung haben. Da diese meist subjektiv wird sie selten indexiert und kann somit nicht von Suchmaschinen wiedergefunden werden. In einigen Bereichen ist aber die Wahl des gesuchten Bildes vom emotionalen Ausdrucks abhängig. Das Ziel dieser Arbeit ist es, Methoden zu untersuchen um Bilder anhand ihres emotionalen Ausdrucks zu klassifizieren. Dabei werden theoretische und empirische Konzepte aus der Psychologie, Kunst und Digitaler Bildverarbeitung verwendet.Wir setzen theoretische Konzepte in mathematische Formeln um und berechnen spezifische Merkmale der Bilder. Diese werden dann von Machine Learning Algorithmen verarbeitet um eine Klassifizierung anhand von Emotionen zu erzielen.Mit unseren Merkmalen erzielen wir bessere Resultate als vergleichbare Publikationen in diesem Bereich.Images speak more than thousand words. One of their aspects is that they can aect people on an emotional level. Since the emotions that arise in the viewer of an image are highly subjective, they are rarely indexed. However there are situations when it would be helpful if images could be retrieved based on their emotional content.Our goal is to use image-processing methods to investigate or develop methods to extract and combine low-level features that represent the emotional content of an image and build a framework that classifies images automatically.Specifically, we exploit theoretical and empirical concepts from psychology and art theory to extract image features that are specific to the domain of artworks with emotional expression.For our work we choose a dimensional approach to emotions that is known from the field of psychophysiology. According to this approach an emotion can be classified by coordinates in a two-dimensional emotion space where one axis represents valence (the type of emotion), ranging from pleasant to unpleasant, and the second axis is defined as arousal (the intensity of the emotion), ranging from calm to exciting/thrilling. Emotions mapped onto this space can be translated into words like angry, sad, exciting etc. and these can be used for automatic indexing of images.Machine learning methods are used to learn classification based on these features.For testing and training, we use three types of data sets, the International Aective Picture System (IAPS) (which is also used by Yanulevskaya et al.), a set of artistic photography downloaded from a photo sharing site (to investigate whether the conscious use of colors and textures displayed by the artists improves the classification) and a set of peer rated abstract paintings to investigate the influence of the features' performance and ratings on pictures without contextual content. Improved classification results are obtained on the IAPS set, compared to Yanulevskaya et al., who use general purpose image features.11

Providing feedback on emotional experiences and decision making

We present a novel lifelog system concept created to provide a human user with feedback on their conscious and unconscious emotional reactions and encourage the process of self-reflection by looking into an affective mirror. The emotion of the user is deduced from biometric data and enhanced by affective sound analysis and facial expression recognition of faces of the weares conversational partners. These high-level analyses also offer information about the social context of the user. All of the recorded data is processed using a novel modification of the spreading-activation theory which is used to model human-like associative thinking. "(c) 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works." Published version is available at: http://dx.doi.org.ezp.biblio.unitn.it/10.1109/AFRCON.2011.6072130. Machajdik, Jana; Stottinger, Julian; Danelova, Ester; Pongratz, Martin; Kavicky, Lukas; Valenti, Roberto; Hanbury, Allan; , "Providing feedback on emotional experiences and decision making," AFRICON, 2011 , vol., no., pp.1-6, 13-15 Sept. 2011 doi: 10.1109/AFRCON.2011.6072130. - (ISBN: 978-1-61284-992-8, ISSN: 2153-0025)

Spectral ellipsometry of La1-xMnO3 films with different degree of epitaxy

The dependence of the optical spectra of La1-xMnO3 films on the degree of epitaxy was investigated. Films of La1-xMnO3-δ (x ≈ 0.1) were grown by metal organic chernical vapor deposition on SrTiO3 and Al2O3 (r-plane cut) substrates. The films are supposed to possess a different degree of epitaxy because of various matching conditions between substrate and film lattices. The optical spectra were obtained in the range 0.5-5.0 eV by spectroscopic ellipsometry technique making use of photometric ellipsometer. Fine structure in the spectra of pseudodielectric function is discussed taking into account the excitations of Drude-type free electrons along with the charge-transfer 2 p(O) → 3d(Mn) and dipole-forbidden d-d(Mn3+) transitions. In this model the difference in the spectra of two type samples with different degree of epitaxy was considered

Growth of Ru and RuO2 films by metal-organic chemical vapour deposition

We have prepared RuO2 layers by metal organic chemical vapour deposition using liquid delivery source and by thermal evaporation of powder precursors. The films were prepared on silicon and r-plane cut sapphire substrates. We discuss thermodynamics of both types of MOCVD techniques. Liquid delivery source technique using diglyme solvent results in deposition of metallic Ru film with some traces of RuO2, while films prepared by thermal evaporation of powder precursors consist of pure RuO2 phase. Thermal evaporation MOCVD grown RuO2 films exhibit excellent electrical properties ; room temperature resistivity of 30 µΩ.cm and residual resistivity ratio between 8 and 30