Image Storage, Indexing and Recognition with Finite State Automata.
Marian Mindek, Michal Burda · International MultiConference of Engineers and Computer Scientists · 2006
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 a weighted finite state automaton as a tool for image loss or loss-free compression, where a resultant automaton contains some useful information. In (4) and (6) one can found an automata-based technique for simple bi-level images coding. This approach is later generalized for gray-scale images and for simple color images (5). Finally, (32) proposes wavelet compression approach. This article is based on ideas presented in papers enumerated above. We describe here an approach to image compression and storage based on finite automata. Such way an image database could be created. Such database could be used to obtain additional information from images without any effort since finite state automata approach generates such information implicitly. For simplicity, we do not describe compression or other useful transformations of an automaton (e.g. wavelet or other).