In recent years, Sparse Representation (SR) and Dictionary Learning (DL) have emerged as powerful tools for efficiently processing of image and video data in non-traditional ways. An area of promise for these theories is object recognition. In this paper, we present an overview of SR and DR and examine several interesting object recognition approaches using these theories. We will also explore the use of non-linear kernel SR as well as DL methods in many computer vision problems including object recognition, multimodal biometrics recognition, and domain adaptation.
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