VAST MM: multimedia browser for presentation video
Proceedings of the 6th ACM international conference on Image and video retrieval
Semantic keyword extraction via adaptive text binarization of unstructured unsourced video
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Selecting the best faces to index presentation videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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The exponential diffusion of unstructured multimedia content on sites such as YouTube has lately fostered a rapid growth of interest in the multimedia community toward a new line of systems to recognize visual content "in the wild", that is, in less structured, unconstrained and more realistic domains. Due to the lack of structure and to the low quality of the data, algorithms and paradigms designed for professional content often cannot directly be applied to the aforementioned domains, thus presenting a new challenge. One particular instance of such "wild videos" is represented by unstructured presentation videos, which are tools nowadays employed in a large variety of systems for different purposes, spanning from distance or e-learning to generation of conference proceedings, from corporate talks to student presentations. We are interested in helping users efficiently and effectively access visual information, in particular technical information, and learn from such online multimedia sources. Our proposed system aims at summarizing, indexing, cross- referencing and browsing unstructured ("wild") presentation videos, with a focus on the quality of the produced indexes as perceived by the end users.