Recent research shows that multimedia resources in the wild are growing at a staggering rate. The rapid increase number of multimedia resources has brought an urgent need to develop intelligent methods to organize and process them. In this paper, the semantic link network model is used for organizing multimedia resources. A whole model for generating the association relation between multimedia resources using semantic link network model is proposed. The definitions, modules, and mechanisms of the semantic link network are used in the proposed method. The integration between the semantic link network and multimedia resources provides a new prospect for organizing them with their semantics. The tags and the surrounding texts of multimedia resources are used to measure their semantic association. The hierarchical semantic of multimedia resources is defined by their annotated tags and surrounding texts. The semantics of tags and surrounding texts are different in the proposed framework. The modules of semantic link network model are implemented to measure association relations. A real data set including 100 thousand images with social tags from Flickr is used in our experiments. Two evaluation methods, including clustering and retrieval, are performed, which shows the proposed method can measure the semantic relatedness between Flickr images accurately and robustly.
Color, as a notable and stable attribute of vehicles, can serve as a useful and reliable cue in a variety of applications in intelligent transportation systems. Therefore, vehicle color recognition in natural scenes has become an important research topic in this area. In this paper, we propose a deep-learning-based algorithm for automatic vehicle color recognition. Different from conventional methods, which usually adopt manually designed features, the proposed algorithm is able to adaptively learn representation that is more effective for the task of vehicle color recognition, which leads to higher recognition accuracy and avoids preprocessing. Moreover, we combine the widely used spatial pyramid strategy with the original convolutional neural network architecture, which further boosts the recognition accuracy. To the best of our knowledge, this is the first work that employs deep learning in the context of vehicle color recognition. The experiments demonstrate that the proposed approach achieves superior performance over conventional methods.Index Terms-Color recognition, deep learning, convolutional neural network (CNN), spatial pyramid (SP), intelligent transportation.
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