Biblio
Nearest neighbor search algorithm plays a very important role in computer image algorithm. When the search data is large, we need to use fast search algorithm. The current fast retrieval algorithms are tree based algorithms. The efficiency of the tree algorithm decreases sharply with the increase of the data dimension. In this paper, a local integral hash nearest neighbor algorithm of the spatial space is proposed to construct the tree structure by changing the way of the node of the access tree. It is able to express data distribution characteristics. After experimental testing, this paper achieves more efficient performance in high dimensional data.
Map-based services are becoming increasingly important in many applications. These services often need to show geospatial objects (e.g., cities and parks) in Web browsers, and being able to retrieve such objects efficiently is critical to achieving a low response time for user queries. In this demonstration we present a browser-based caching technique to store and load geospatial objects on a map in a Web page. The technique employs a hierarchical structure to store and index polygons, and does intelligent prefetching and cache replacement by utilizing the information about the user's recent browser activities. We demonstrate the usage of the technique in an application called TwitterMap for visualizing more than 1 billion tweets in real time. We show its effectiveness by using different replacement policies. The technique is implemented as a general-purpose Javascript library, making it suitable for other applications as well.