Optimizing progressive query-by-example over pre-clustered large image databases

The typical mode for querying in an image content-based information system is query-by-example, which allows the user to provide an image as a query and to search for similar images (i.e., the nearest neighbors) based on one or a combination of low-level multidimensional features of the query example. Off-line, this requires the time-consuming pre-computing of the whole set of visual descriptors over the image database. On-line, one major drawback is that multidimensional sequential NN-search is usually exhaustive over the whole image set face to the user who has a very limited patience. In this paper, we propose a technique for improving the performance of image queryby-example execution strategies over multiple visual features. This includes first, the pre-clustering of the large image database and then, the scheduling of the processing of the feature clusters before providing progressively the query results (i.e., intermediate results are sent continuously before the end of the exh...

• Type: Conference
• Year: 2005
• Where: CVDB
• Authors: Anicet Kouomou Choupo, Laure Berti-Equille, Annie Morin
• Keywords: Computer Vision, CVDB 2005, Image, Image Content-based Information, Image Database