Speeding up the similarity search in high-dimensional image database by multiscale filtering and dynamic programming

This paper presents a scalable content-based image indexing and retrieval system based on a new multiscale filter. Image databases often represent the image objects as high-dimensional feature vectors and access them via the feature vectors and similarity measure. A similarity measure based on the proposed multiscale filtering technique is defined to reduce the computational complexity of the similarity search in high-dimensional image database. Moreover, a special attention is paid to solve the problem of feature value correlation by dynamic programming. This problem arises from changes of images due to database updating or considering spatial layout in constructing feature vectors. The computational complexity of similarity measure in high-dimensional image database is very huge and the applications of image retrieval are restricted to certain areas. To demonstrate the effectiveness of the proposed algorithm, we conducted extensive experiments and compared the performance with the IBM's query by image content (QBIC) and Jain and Vailaya's methods. The experimental results demonstrate that the proposed method outperforms both of the methods in retrieval accuracy and noise immunity. The execution speed of the proposed method is much faster than that of QBIC method and it can achieve good results in terms of retrieval accuracy compared with Jain's method and QBIC method.