Finding emotional-laden resources on the World Wide Web

Some content in multimedia resources can depict or evoke certain emotions in users. The aim of Emotional Information Retrieval (EmIR) and of our research is to identify knowledge about emotional-laden documents and to use these findings in a new kind of World Wide Web information service that allows users to search and browse by emotion. Our prototype, called Media EMOtion SEarch (MEMOSE), is largely based on the results of research regarding emotive music pieces, images and videos. In order to index both evoked and depicted emotions in these three media types and to make them searchable, we work with a controlled vocabulary, slide controls to adjust the emotions’ intensities, and broad folksonomies to identify and separate the correct resource-specific emotions. This separation of so-called power tags is based on a tag distribution which follows either an inverse power law (only one emotion was recognized) or an inverse-logistical shape (two or three emotions were recognized). Both distributions are well known in information science. MEMOSE consists of a tool for tagging basic emotions with the help of slide controls, a processing device to separate power tags, a retrieval component consisting of a search interface (for any topic in combination with one or more emotions) and a results screen. The latter shows two separately ranked lists of items for each media type (depicted and felt emotions), displaying thumbnails of resources, ranked by the mean values of intensity. In the evaluation of the MEMOSE prototype, study participants described our EmIR system as an enjoyable Web 2.0 service

MC2: MPEG-7 content modelling communities

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe use of multimedia content on the web has grown significantly in recent years. Websites such as Facebook, YouTube and Flickr cater for enormous amounts of multimedia content uploaded by users. This vast amount of multimedia content requires comprehensive content modelling otherwise retrieving relevant content will be challenging. Modelling multimedia content can be an extremely time consuming task that may seem impossible particularly when undertaken by individual users. However, the advent of Web 2.0 and associated communities, such as YouTube and Flickr, has shown that users appear to be more willing to collaborate in order to take on enormous tasks such as multimedia content modelling. Harnessing the power of communities to achieve comprehensive content modelling is the primary focus of this research. The aim of this thesis is to explore collaborative multimedia content modelling and in particular the effectiveness of existing multimedia content modelling tools, taking into account the key development challenges of existing collaborative content modelling research and the associated modelling tools. Four research objectives are pursued in order to achieve this; first, design a user experiment to study users’ tagging behaviour with existing multimedia tagging tools and identify any relationships between such user behaviour; second, design and develop a framework for MPEG-7 content modelling communities based on the results of the experiment; third, implement an online service as a proof of concept of the framework; fourth, validate the framework through the online service during a repeat of the initial user experiment. This research contributes first, a conceptual model of user behaviour visualised as a fuzzy cognitive map and, second, an MPEG-7 framework for multimedia content modelling communities (MC2) and its proof of concept as an online service. The fuzzy cognitive model embodies relationships between user tagging behaviour and context and provides an understanding of user priorities in the description of content features and the relationships that exist between them. The MC2 framework, developed based on the fuzzy cognitive model, is deep-rooted in user content modelling behaviour and content preferences. A proof of concept of the MC2 framework is implemented as an online service in which all metadata is modelled using MPEG-7. The online service is validated, first, empirically with the same group of users and through the same experiment that led to the development of the fuzzy cognitive model and, second, functionally against the folksonomy and MPEG-7 content modelling tools used in the initial experiment. The validation demonstrates that MC2 has the advantages without the shortcomings of existing multimedia tagging tools by harnessing the ease of use of folksonomy tools while producing comprehensive structured metadata.Supported by UK Engineering and Physical Sciences Research Council (EPSRC

Music video affective understanding using feature importance analysis

Music video is a popular type of entertainment by viewers. Currently, the novel indexing and retrieval approach based on the affective cues contained in music videos becomes more and more attractive to users. Music video affective analysis and understanding is one of the most popular topics in current multimedia community. In this paper, we propose a novel feature importance analysis approach to select most representative arousal and valence features for arousal and valence modeling. Compared with state-of-the-art work by Zhang on music video affective analysis, our main contributions are in the following aspects: (1) Another 3 affect-related features are extracted to enrich the feature set and exploit their correlation with arousal and valence. (2) All extracted features are ordered via feature importance analysis, and then optimal feature subset is selected after ordering. (3) Different regression methods are compared for arousal and valence modeling in order to find the fittest estimation function. Our method achieves 33.39% and 42.17% deduction in terms of mean absolute error compared with Zhang's method. Experimental results demonstrate our proposed method has a considerable improvement on music video affective understanding