Content-based image retrieval speedup
This work addresses efficiency issues for users of CBIR systems, but it is incremental as it builds on existing acceleration techniques.
The paper tackles the problem of slow content-based image retrieval in large databases by introducing a method that uses Zernike moments and interval calculations to reduce the database before retrieval, speeding up the process while maintaining accuracy.
Content-based image retrieval (CBIR) is a task of retrieving images from their contents. Since retrieval process is a time-consuming task in large image databases, acceleration methods can be very useful. This paper presents a novel method to speed up CBIR systems. In the proposed method, first Zernike moments are extracted from query image and an interval is calculated for that query. Images in database which are out of the interval are ignored in retrieval process. Therefore, a database reduction occurs before retrieval which leads to speed up. It is shown that in reduced database, relevant images to query image are preserved and irrelevant images are throwed away. Therefore, the proposed method speed up retrieval process and preserve CBIR accuracy, simultaneously.