Sparse Representation forComputerVision and Pattern Recognition A relatively small sample of computer vision and pattern recognition information in applications such as face recognition is often sufficient to reveal the meaning the user desires.

Techniques from sparse signal representation are beginning to see significant impact in computer vision, often on nontraditional applications where the goal is not just to obtain a compact high-fidelity representation of the observed signal, but also to extract semantic information. The choice of dictionary plays a key role in bridging this gap: unconventional dictionaries consisting of, or learned from, the training samples themselves provide the key to obtaining state-of-the-art results and to attaching semantic meaning to sparse signal represen- tations. Understanding the good performance of such uncon- ventional dictionaries in turn demands new algorithmic and analytical techniques. This review paper highlights a few

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