Discrete cosine transform: algorithms, advantages, applications
Discrete cosine transform: algorithms, advantages, applications
Ten lectures on wavelets
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Combined techniques of singular value decomposition and vector quantization for image coding
IEEE Transactions on Image Processing
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When we seek to directly learn basis functions from natural scenes, we are confronted with the problem of simultaneous estimation of these basis functions and the coefficients of each image (when projected onto that basis). In this work, we are mainly interested in learning matrix space basis functions and the projection coefficients from a set of natural images. We cast this problem in a joint optimization framework. The Frobenius norm is used to express the distance between a natural image and its matrix space reconstruction. An alternating algorithm is derived to simultaneously solve for the basis vectors and the projection coefficients. Since our fundamental goal is classification and indexing, we develop a matrix space distance measure between images in the training set. Results are shown on face images and natural scenes.