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Type of Document Thesis Author Cheng, Lei Author's Email Address leicheng2001@hotmail.com URN etd-04072004-184239 Title Sparse Representations For Recognition Degree Master of Science Department Computer Science, Department of Advisory Committee
Advisor Name Title Xiuwen Liu Committee Chair David Whalley Committee Member Mike Burmester Committee Member Keywords
- Independent Spectral Representations
- Image Recognition
- Sparse Linear Representations
Date of Defense 2003-11-10 Availability unrestricted Abstract In recent years, studies have shown that independent/sparse components of local windowsof natural images resemble the receptive fields of cells in the early stages of the mammalian
visual pathway. However, the role of the independence/sparseness in visual recognition is not well understood. In the first part of this thesis, we argue that the independence/sparseness
resolves the curse of dimensionality by reducing the complexity of probability models to
the linear order of the dimension. In addition, we show empirically that the complexity
reduction does not deteriorate the recognition performance on all the datasets we have
used based on proposed independent spectral representation. This study provides the first
empirical evidence on the effectiveness of sparse representations for recognition. In the
second part of this thesis, we address this question systematically by providing an algorithm
for finding sparse representations that are effective for recognition. Although sparse coding
has been regarded as an important principle for recognition which has been used effectively
to derive filters with desirable properties, there is no effective algorithm to link the sparse coding principle to the recognition performance. By proposing a criterion consisting of weighted combination of recognition performance and sparseness, an optimal sparse linear representation with good recognition performance is achieved by using a Monte-Carlo
simulated annealing algorithm. Moreover, we also find an interesting relationship among
commonly used linear representations by comparing them based on both sparseness and
recognition performance.
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