Efficient image database retrieval using wavelet packets and principal component analysis

Mohammed Saeed; Hamid Rabiee

doi:10.1117/12.366830

26 October 1999 Efficient image database retrieval using wavelet packets and principal component analysis

Mohammed Saeed, Hamid Rabiee

Proceedings Volume 3813, Wavelet Applications in Signal and Image Processing VII; (1999) https://doi.org/10.1117/12.366830
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

Abstract

With the advancement of multimedia technology and the internet, numerous applications have arisen which require the storage and retrieval of large image and video databases. A novel method (Eigenwavelet) was developed to retrieve images from a large heterogeneous image database upon a user-specified query. The queries are in the form of an image(s) that the user seeks to find similar matches to in the database. Using the queries, an efficient algorithm was developed which decomposed each image in the database using wavelet packet analysis. Along each node of the packet tree, Principal Component Analysis was applied to the database images after wavelet packet decomposition, and a set of eigenvectors were generated for each node of the packet tree. To search the image database, the query images are projected onto these eigenvectors (Eigenwavelet coefficients). A distance metric is computed between the projections of the queries and the projections of the images in the database onto the eigenwavelets. Those images with minimal distance (L1) are retrieved in response to a unique query set. Simulations with a heterogeneous image database suggest the Eigenwavelet method of image retrieval is a robust and computationally tractable method of retrieving images with a probability of detection >.8.

Citation Download Citation

Mohammed Saeed and Hamid Rabiee "Efficient image database retrieval using wavelet packets and principal component analysis", Proc. SPIE 3813, Wavelet Applications in Signal and Image Processing VII, (26 October 1999); https://doi.org/10.1117/12.366830

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