Sliding Window Recursive HAPCA for 3D Image Decomposition

The famous method Principal Components Analysis (PCA) is the basic approach for decomposition of 3D tensor images (for example, multiand hyper-spectral, multi-view, computer tomography, video, etc.). As a result of the processing, their information redundancy is significantly reduced. This is of high importance for the efficient compression and for the reduction of the features space needed, when object recognition or search is performed. The basic obstacle for the wide application of PCA is the high computational complexity. One of the approaches to overcome the problem is to use algorithms, based on the recursive PCA. The wellknown methods for recursive PCA are aimed at the processing of sequences of images, represented as nonoverlapping groups of vectors. In this work is proposed new method, called Sliding Recursive Hierarchical Adaptive PCA, based on image sequence processing in a sliding window. The new method decreases the number of calculations needed, and permits parallel implementation. The results obtained from the algorithm simulation, confirm its efficiency. The lower computational complexity of the new method facilitates its application in the real-time processing of 3D tensor images. Key-Words: Hierarchical Adaptive PCA, Sliding Recursive PCA, 3D tensor image decomposition.