Multimedia Tools

In the context of distance education, the choice of platform for content delivery becomes a critical success factor. This paper analyzes the state of the art of e-learning platforms, Learning Management Systems and sums up a comparative analysis of some of the most popular open-source and proprietary. I test results on the comparison between the 27 different platforms and e-learning, show that at present there is still a significant  gap  between open source and proprietary platforms, particularly with regard to the characteristics of social networks.

A multimedia content is composed of several streams that carry information in audio, video or textual channels. Classification and clustering multimedia contents require extraction and combination of information from these streams. The streams constituting a multimedia content are naturally different in terms of scale, dynamics and temporal patterns. These differences make combining the information sources using classic combination techniques difficult. We propose an asynchronous feature level fusion approach that creates a unified hybrid feature space out of the individual signal measurements. The target space can be used for clustering or classification of the multimedia content. As a representative application, we used the proposed approach to recognize basic affective states from speech prosody and facial expressions. Experimental results over two audiovisual emotion databases with 42 and 12 subjects revealed that the performance of the proposed system is significantly higher than the unimodal face based and speech based systems, as well as synchronous feature level and decision level fusion approaches.

In this paper, we describe the development of a new and innovative tool of video mashup. This application is an easy to use tool of video editing integrated in a cross-media platform; it works taking the information from a repository of videos and puts into action a process of semi-automatic editing supporting users in the production of video mashup. Doing so it gives vent to their creative side without them being forced to learn how to use a complicated and unlikely new technology. The users will be further helped in building their own editing by the intelligent system working behind the tool: it combines semantic annotation (tags and comments by users), low level features (gradient of color, texture and movements) and high level features (general data distinguishing a movie: actors, director, year of production, etc.) to furnish a pre-elaborated editing users can modify in a very simple way.

Having effective methods to access the desired images is essential nowadays with the availability of a huge amount of digital images. The proposed approach is based on an analogy between content-based image retrieval and text retrieval. The aim of the approach is to build a meaningful mid-level representation of images to be used later on for matching between a query image and other images in the desired database. The approach is based firstly on constructing different visual words using local patch extraction and fusion of descriptors. Secondly, we introduce a new method using multilayer pLSA to eliminate the noisiest words generated by the vocabulary building process. Thirdly, a new spatial weighting scheme is introduced that consists of weighting visual words according to the probability of each visual word to belong to each of the n Gaussian. Finally, we construct visual phrases from groups of visual words that are involved in strong association rules. Experimental results show that our approach outperforms the results of traditional image retrieval techniques.

With the advent of the ubiquitous era, many studies have been devoted to various situation-aware services in the semantic web environment. One of the most challenging studies involves implementing a situation-aware personalized music recommendation service which considers the user’s situation and preferences. Situation-aware music recommendation requires multidisciplinary efforts including low-level feature extraction and analysis, music mood classification and human emotion prediction. In this paper, we propose a new scheme for a situation-aware/user-adaptive music recommendation service in the semantic web environment. To do this, we first discuss utilizing knowledge for analyzing and retrieving music contents semantically, and a user adaptive music recommendation scheme based on semantic web technologies that facilitates the development of domain knowledge and a rule set. Based on this discussion, we describe our Context-based Music Recommendation (COMUS) ontology for modeling the user’s musical preferences and contexts, and supporting reasoning about the user’s desired emotions and preferences. Basically, COMUS defines an upper music ontology that captures concepts on the general properties of music such as titles, artists and genres. In addition, it provides functionality for adding domain-specific ontologies, such as music features, moods and situations, in a hierarchical manner, for extensibility. Using this context ontology, we believe that logical reasoning rules can be inferred based on high-level (implicit) knowledge such as situations from low-level (explicit) knowledge. As an innovation, our ontology can express detailed and complicated relations among music clips, moods and situations, which enables users to find appropriate music. We present some of the experiments we performed as a case-study for music recommendation.

This paper presents the use of video parsing and video analysis techniques to attain a radical change in Content Analysis processes. Content Analysis provides frameworks for a better understanding of information, mass media information in particular. The goals of the work presented in this paper are to assess the feasibility of designing Content Analysis processes under much faster and much

In the field of automatic audiovisual content-based indexing and structuring, finding events like interviews, debates, reports, or live commentaries requires to bridge the gap between low-level feature extraction and such high-level event detection. In our work, we consider that detecting speaker roles like Anchor, Journalist and Other is a first step to enrich interaction sequences between speakers. Our work relies on the assumption of the existence of clues about speaker roles in temporal, prosodic and basic signal features extracted from audio files and from speaker segmentations. Each speaker is therefore represented by a 36-feature vector. Contrarily to most of the state-of-the-art propositions we do not use the structure of the document to recognize the roles of the interveners. We investigate the influence of two dimensionality reduction techniques (Principal Component Analysis and Linear Discriminant Analysis) and different classification methods (Gaussian Mixture Models, K-nearest neighbours and Support Vectors Machines). Experiments are done on the 13-h corpus of the ESTER2 evaluation campaign. The best result reaches about 82% of well recognized roles. This corresponds to more than 89% of speech duration correctly labelled.

Perceptual analysis of video (analysis by unaided ear and eye) plays an important role in such disciplines as psychology, psycholinguistics, linguistics, anthropology, and neurology. In the specific domain of psycholinguistic analysis of gesture and speech, researchers micro-analyze videos of subjects using a high quality video cassette recorder that has a digital freeze capability down to the specific frame. Such analyses are very labor intensive and slow. We present a multimedia system for perceptual analysis of video data using a multiple, dynamically linked representation model. The system components are linked through a time portal with a current time focus. The system provides mechanisms to analyze overlapping hierarchical interpretations of the discourse, and integrates visual gesture analysis, speech analysis, video gaze analysis, and text transcription into a coordinated whole. The various interaction components facilitate accurate multi-point access to the data. While this system is currently used to analyze gesture, speech and gaze in human discourse, the system described may be applied to any other field where careful analysis of temporal synchronies in video is important.

Virtual Reality (VR) has been constantly evolving since its early days, and is now a fundamental technology in different application areas. User evaluation is a crucial step in the design and development of VR systems that do respond to users’ needs, as well as for identifying applications that indeed gain from the use of such technology. Yet, there is not much work reported concerning usability evaluation and validation of VR systems, when compared with the traditional desktop setup. The paper presents a user study performed, as a first step, for the evaluation of a low-cost VR system using a Head-Mounted Display (HMD). That system was compared to a traditional desktop setup through an experiment that assessed user performance, when carrying out navigation tasks in a game scenario for a short period. The results show that, although users were generally satisfied with the VR system, and found the HMD interaction intuitive and natural, most performed better with the desktop setup.

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