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Shadow removing is a key issue for moving objects detection in a surveillance systems. However, few research address this problem by a learning strategy. In this paper, we present a multi-resolution classification method to remove shadows from the object detection result. Because the number of samples which denote the shadow and object is reasonably large, we adopt a coarse-to fine strategy during the classification process. By partitioning feature space into hypercubes according to different resolutions, we train a group of classifiers which can label the samples for testing from coarse to fine. Support Vector Machines are chosen in the process of training and the hypercubes which represent support vectors are subdivided in order to generate the sample set intended for training in a higher resolution. Because of the conglomeration property of the samples to be tested, we can label most of the samples using the simple classifiers trained at low resolution. In some cases, the method presented in this paper can reduce the computational complex of the classification algorithm. Finally, experimental results have substantiated the effectiveness of the proposed method.

Abstract. Searching a file by its name is an essential problem of a large peerto-peer file-sharing system. Napster and Gnutella have poor scalability. DHTbased systems do not keep the order of the keys. Skip graphs and SkipNet have too many links. In this paper, we present a new scalable distributed data structure LinkNet for searching in a large peer-to-peer system. In LinkNet, all elements are stored in a sorted doubly linked list, and one node stores many elements. LinkNet uses virtual link to speed search and enhance fault tolerance. Because LinkNet is based on a sorted list, it benefits operations such as range query, bulk loading of data, and merging of two LinkNets.

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