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The retrieval and recommendation of social media have provided an immense opportunity to exploit the collective behavior of community users through linked multi-modal data, such as images and tags, where tags provide context information, and images represent visual content. The stability of content information is more reliable than user contributed context information, which was ignored by many existing...
As visual recognition scales up to ever larger numbers of categories, maintaining high accuracy is increasingly difficult. In this work, we study the problem of optimizing accuracy-specificity trade-offs in large scale recognition, motivated by the observation that object categories form a semantic hierarchy consisting of many levels of abstraction. A classifier can select the appropriate level, trading...
Panoramic street view is now becoming a popular service in digital map due to its expedient of virtual walking through. Recently, some geo-tagged photos have been added into street view as additional illustration images for the same scenes. However, these images come directly from certain photo sharing websites where users manually tag the locations of their uploaded images; the quality of annotated...
A novel brain computer interface (BCI) is implemented in this study, which only depends on auditory modality. The subjects voluntary recognition of the property (e.g. voice laterality) of a target human voice makes the discriminability between brain responses to target and non-target voices in a random sequence. EEG data from eight subjects showed that the amplitude of N2 and late positive component...
Using image classification approach for automatic image annotation is one promising method. In order to improve image annotation accuracy, recent researchers propose to use AdaBoost algorithm for the ensemble of classifiers. But in these researches, only fewer features are used. We construct multi-class classifiers for all the image low-level feature of multimedia content description interface and...
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