Sparse and Redundant Modeling of Image Content Using an Image-Signature-Dictionary

Modeling signals by sparse and redundant representations has been drawing considerable attention in recent years. Coupled with the ability to train the dictionary using signal examples, these techniques have been shown to lead to state-of-the-art results in a series of recent applications. In this paper we propose a novel structure of such a model for representing image content. The new dictionary is itself a small image, such that every patch in it (in varying location and size) is a possible atom in the representation. We refer to this as the image-signature-dictionary (ISD) and show how it can be trained from image examples. This structure extends the well-known image and video epitomes, as introduced by Jojic, Frey, and Kannan [in Proceedings of the IEEE International Conference on Computer Vision, 2003, pp. 34

• Type: Journal
• Year: 2008
• Where: SIAMIS
• Authors: Michal Aharon, Michael Elad
• Keywords: Computer Vision, Image, Redundant Representations, SIAMIS 2008