Welcome to the InfoSci Platform
A Novel Approach in Adopting Finite State Automata for Image Processing Applications

A Novel Approach in Adopting Finite State Automata for Image Processing Applications

R. Obulakonda Reddy, Kashyap D. Dhruve, R. Nagarjuna Reddy, M. Radha, N. Sree Vani
Copyright: © 2018 |Volume: 8 |Issue: 1 |Pages: 16
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781522545712|DOI: 10.4018/IJCVIP.2018010104
Cite Article Cite Article

MLA

Reddy, R. Obulakonda, et al. "A Novel Approach in Adopting Finite State Automata for Image Processing Applications." IJCVIP vol.8, no.1 2018: pp.59-74. http://doi.org/10.4018/IJCVIP.2018010104

APA

Reddy, R. O., Dhruve, K. D., Reddy, R. N., Radha, M., & Vani, N. S. (2018). A Novel Approach in Adopting Finite State Automata for Image Processing Applications. International Journal of Computer Vision and Image Processing (IJCVIP), 8(1), 59-74. http://doi.org/10.4018/IJCVIP.2018010104

Chicago

Reddy, R. Obulakonda, et al. "A Novel Approach in Adopting Finite State Automata for Image Processing Applications," International Journal of Computer Vision and Image Processing (IJCVIP) 8, no.1: 59-74. http://doi.org/10.4018/IJCVIP.2018010104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

This article describes how robust image processing application rely heavily on image descriptors extracted. Limited work is carried out in adopting probabilistic finite state automata (PFSA) models for image processing. A finite state automata for image processing (FSAFIP) method is presented here. Texture classification and content based image retrieval (CBIR) is considered. In FSAFIP, foreground and background regions of an image are identified and later split into patches. Using a tristate PFSA model, feature descriptors corresponding to background/foreground regions are constructed. A distance based large margin nearest neighbor (LMNN) classifier is considered in FSAFIP to impart intelligence. A performance and experimental study to evaluate performance of FSAFIP for CBIR and texture classification is presented. Comparison results in CBIR obtained prove superior performance of FSAFIP over existing methods on Corel-1K dataset. High texture classification accuracy of 99.2% is reported using FSAFIP on KHT-TIPS dataset. An improved texture classification accuracy is achieved using FSAFIP in comparison to former methods.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.