Nowadays, tags play an important role in the search and retrieval process in multimedia content sharing social networks. As the amount of multimedia contents explosively increases, it is a challenging problem to find a content that will be appealing to the users. Furthermore, the retrieval of multimedia contents, which can match users' current mood or affective state, can be of great interest. One approach to indexing multimedia contents is to determine the potential affective state, which they can induce in users. In this paper, multimedia content analysis is performed to extract affective audio and visual cues from different music video clips. Furthermore, several fusion techniques are used to combine the information extracted from the audio and video contents of music video clips. We show that using the proposed methodology, a relatively high performance (up to 90%) of affect recognition is obtained.

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The use of visual information derived from accurate lip extraction, can provide features invariant to noise perturbation for speech recognition systems and can be also used in a wide variety of applications. Unlike many current automatic lip reading systems which impose several restrictions on users, our efforts are towards an unconstrained system. In this paper we introduce a method using k-means color clustering with automatically adapted number of clusters, for the extraction of the lip area. The method's performance is improved by previously applying nearest neighbor color segmentation. The extracted lip area is morphologically processed and fitted by a best-fit ellipse. The points of interest (keypoints) of the mouth area are extracted, while a corner detector for fine tuning of mouth corners is applied. Experimental tests have shown that the algorithm works very well under natural conditions and accurate extraction of lip keypoints is feasible.

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