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community. Interest communities will reorganize as any user's interest has changed. Simulation results show this model has higher search efficiency and lower search cost compared with traditional keyword search algorithm.
There are currently two interface types for searching and browsing large image collections: keyword-based image retrieval (KBIR), and content-based image retrieval (CBIR). The KBIR system searches images according to the text of keyword annotated on images. This method is simple and relative effective to the query
In the past few years, videos become an ordinary communication mean for both personal and business activities. Not only the keyword search that have been utilized widely, but also the video content-based search, i.e. given a query video, the similar video sequences can be retrieved. Meanwhile, the increasing of the
. Especially as a part of the interest router table, each K-bucket stores a certain number of the peers' information that have high interest similarity. The query can be executed in the appropriate k-bucket by calculating interest similarity and interest keyword. Through mining the latent interest, we found that two peers having
mechanism to create a codebook with low network cost. Since the number of features in each image is large, compared to a text query generally consisting of several keywords, information exchange between nodes for each query image generates high network cost. In order to further reduce the network cost, we implement two static
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.