Bioingenium at ImageClefmed 2010: A Latent Semantic Approach.
Conference: CLEF 2010 LABs and Workshops, Notebook Papers, 22-23 September 2010, Padua, Italy
Full-textDOI: · Available from: Fabio A. González, Jan 02, 2014
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Nature 01/1999; · 42.35 Impact Factor
Conference Paper: Summarizing video using non-negative similarity matrix factorization[Show abstract] [Hide abstract]
ABSTRACT: We present a novel approach to automatically extracting summary excerpts from audio video and video. Our approach is to maximize the average similarity between the excerpt and the source. We first calculate a similarity matrix by comparing each pair of time samples using a quantitative similarity measure. To determine the segment with highest average similarity, we maximize the summation of the self-similarity matrix over the support of the segment. To select multiple excerpts while avoiding redundancy, we compute the non-negative matrix factorization (NMF) of the similarity matrix into its essential structural components. We then build a summary comprised of excerpts from the main components, selecting the excerpts for maximum average similarity within each component. Variations integrating segmentation and other information are also discussed, and experimental results are presented.Multimedia Signal Processing, 2002 IEEE Workshop on; 01/2003
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ABSTRACT: We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related elds. In this paper, we survey almost 300 key theoret- ical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and discuss the spawning of related sub-elds in the process. We also discuss signican t challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real-world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.