A Distributed Algorithm for Content Based Indexing of Images by Projections on Ritz Primary Images
Data Mining and Knowledge Discovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustered Blockwise PCA for Representing Visual Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust sequential view planning for object recognition using multiple cameras
Image and Vision Computing
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Finding eigenvectors of a sequence of real images has usually been considered to require too much computation to be practical. Our spatial temporal adaptive (STA) method reduces the computational complexity of the approximate partial eigenvalue decomposition based on image encoding. Spatial temporal encoding is used to reduce storage and computation, and then, singular value decomposition (SVD) is applied. After the adaptive discrete cosine transform (DCT) encoding, blocks that are similar in consecutive images are consolidated. The computational economy of our method was verified by tests on different large sets of images. The results show that this method is 6 to 10 times faster than the traditional SVD method for several kinds of real images. The economy of this algorithm increases with increasing correlation within the image and with increasing correlation between consecutive images within a set. This algorithm is useful for pattern recognition using eigenvectors, which is a research field that has been active recently