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We propose an unsupervised approach to segment color images and annotate its regions. The annotation process uses a multi-modal thesaurus that is built from a large collection of training images by learning associations between low-level visual features and keywords. Association rules are learned through fuzzy
Tattoo images on human body have been routinely collected and used in law enforcement to assist in suspect and victim identification. However, the current practice of matching tattoos is based on keywords. Assigning keywords to individual tattoo images is both tedious and subjective. We have developed a content-based
comparison features in real time. In addition the img(Rummager) application can execute a hybrid search of images from the application server, combining keyword information and visual similarity. Also img(Rummager) supports easy retrieval evaluation based on the normalized modified retrieval rank (NMRR) and average precision
aspects of information that is helpful to locate highlights, we build two algorithms detecting highlight candidates based on audio and video, respectively, where hidden Markov model (HMM) audio keyword modeling and unsupervised shot clustering are applied. Decision fusion is invoked to combine audio and video highlight
knowledge-intensive query approach and simple query approach. The proposed knowledge-intensive query approach successfully retrieves the potential evidences with average 12.33% improved accuracy in contrast to simple query approach. Furthermore, we performed human evaluation to identify the overall satisfaction of the proposed
knowledge-intensive query approach and simple query approach. The proposed knowledge-intensive query approach successfully retrieves the potential evidences with average 12.33% improved accuracy in contrast to simple query approach. Furthermore, we performed human evaluation to identify the overall satisfaction of the proposed
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