Mining based on latent link analysis for large-scale multimedia archive

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We address partial image detection and identification which may correspond to recurrent objects in huge amount of multimedia archives such as large-scale broadcast video archive and Web images and videos. We regard detected and identified partial images as latent link and study mining techniques for graph structure composed of latent links obtained from archives. The outcome of this project enables enumeration of specific objects appear in multimedia archive and their statistical analysis, object auto-annotation based on co-occurrence analysis with metadata and annotation, self-taught object recognition system, and so on. Partial image detection and identification, as the most important technical component, will be implemented by extending our former research outcome of image/video copy detection and mining techniques.