finite state automata

In this paper, we introduce the weighted finite state automata (WFA) as a tool for image specification and loss or loss-free compression. We describe how to compute WFA from input images and how the resultant automaton can be used to store images (or to create image database) and to obtain additional interesting information usable for image indexing or recognition. Next, we describe an automata composition technique. We also present a possible way of storing automata in persistent storage. Finally, we depict some tests. The benefit of our approach is that some beneficial information for indexing and recognition without knowledge of scene does not need to be computed from compressed images using other algorithms since the resultant WFA already contains it.

In this paper, we introduce the weighted finite state automata (WFA) as a tool for image specification and loss or loss-free compression. We describe how to compute WFA from input images and how the resultant automaton can be used to store images (or to create image database) and to obtain additional interesting information usable for image indexing or recognition. Next, we describe an automata composition technique. We also present a possible way of storing automata in persistent storage. Finally, we depict some tests. The benefit of our approach is that some beneficial information for indexing and recognition without knowledge of scene does not need to be computed from compressed images using other algorithms since the resultant WFA already contains it.