Jiewei Cao

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In this paper, we describe our approach as part of the MediaEval 2014 Placing Task evaluation. We first identify tags that are most indicative of geographic location by calculating the spatial-aware weighting for all tags in the training set. These weighting are applied to a language model-based retrieval framework. To address the geo-tagging problem, we(More)
Nowadays the locations of social images play an important role in geographic knowledge discovery. However, most social images still lack the location information, driving location estimation for social images to have recently become an active research topic. With the rapid growth of social images, new challenges have been posed: 1) data quality of social(More)
—Instance retrieval requires one to search for images that contain a particular object within a large corpus. Recent studies show that using image features generated by pooling convolutional layer feature maps (CFMs) of a pretrained convolu-tional neural network (CNN) leads to promising performance for this task. However, due to the global pooling strategy(More)
Recently, neuron activations extracted from a pre-trained convolutional neural network (CNN) show promising performance in various visual tasks. However, due to the domain and task bias, using the features generated from the model pre-trained for image classification as image representations for instance retrieval is problematic. In this paper, we propose(More)
The explosive growth of video content on the Web has been revolutionizing the way people share, exchange and perceive information. At the same time, however, the massive amount of Web video content may degrade user experience in that it is tedious and time-consuming to view relevant videos uploaded by different users and grasp the gist of their content. In(More)
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