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Automatic video keyword generation is one of the key ingredients in reducing the burden of security officers in analyzing surveillance videos. Keywords or attributes are generally chosen manually based on expert knowledge of surveillance. Most existing works primarily aim at either supervised learning approaches
Database (HMDB), a collection of realistic video clips. The detection and localization paradigm we introduce uses a keyword model for detecting key activities or gestures in a video sequence. This process is analogous to the use of keyword or key-phrase detection in speech processing. The method learns models for the
We propose a probabilistic graphical model to represent weakly annotated images. This model is used to classify images and automatically extend existing annotations to new images by taking into account semantic relations between keywords. The proposed method has been evaluated in classification and automatic
In this paper, we propose a new method to select relevant images to the given keywords from the images gathered from the Web. Our novel method is based on the probabilistic latent semantic analysis (PLSA) model, which is a generative probabilistic topic model. Firstly, we gather images related to the given keywords
identifies what the image and accompanying article are about, whereas surface realization determines how to verbalize the chosen content. We approximate content selection with a probabilistic image annotation model that suggests keywords for an image. The model postulates that images and their textual descriptions are generated
eyeball movement. Also, the gradient of pupil size variation is used to detect the transition point between navigational intent and the informational intent. A Naïve Bayes classifier is used as a tool for the extraction of query keywords to search and retrieve specific information from personalized knowledge database
database is annotated with keywords. We present and evaluate a new method which improves the effectiveness of content-based image retrieval, by integrating semantic concepts extracted from text. Our model is inspired from the probabilistic graphical model theory: we propose a hierarchical mixture model which enables to handle
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