%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2016/07.18.20.42.52
%2 sid.inpe.br/sibgrapi/2016/07.18.20.42.53
%@doi 10.1109/SIBGRAPI.2016.057
%T Texture Analysis using Informed Search in Graphs
%D 2016
%A Frutuoso, Romulo Lopes,
%A Gomes, Joao Paulo P.,
%A Santos, Emanuele M. dos,
%A Cavalcante Neto, Joaquim B.,
%A Vidal, Creto A.,
%@affiliation Department of Computer Science, Universidade Federal do Ceara - UFC
%@affiliation Department of Computer Science, Universidade Federal do Ceara - UFC
%@affiliation Department of Computer Science, Universidade Federal do Ceara - UFC
%@affiliation Department of Computer Science, Universidade Federal do Ceara - UFC
%@affiliation Department of Computer Science, Universidade Federal do Ceara - UFC
%E Aliaga, Daniel G.,
%E Davis, Larry S.,
%E Farias, Ricardo C.,
%E Fernandes, Leandro A. F.,
%E Gibson, Stuart J.,
%E Giraldi, Gilson A.,
%E Gois, João Paulo,
%E Maciel, Anderson,
%E Menotti, David,
%E Miranda, Paulo A. V.,
%E Musse, Soraia,
%E Namikawa, Laercio,
%E Pamplona, Mauricio,
%E Papa, João Paulo,
%E Santos, Jefersson dos,
%E Schwartz, William Robson,
%E Thomaz, Carlos E.,
%B Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)
%C São José dos Campos, SP, Brazil
%8 4-7 Oct. 2016
%I IEEE Computer Society´s Conference Publishing Services
%J Los Alamitos
%S Proceedings
%K pattern recognition, texture analysis, search methods, informed search.
%X In this paper we propose a variant oisf the TASPG algorithm for texture recognition. TASPG (Texture Analysis based on Shortest Paths in Graphs) is a recently proposed texture recognition method that extracts features from paths along texture images. Although TASPG achieved promising results, its application may be limited by its high computational cost which stems from the extensive use of Dijkstra's algorithm. In this work, we propose a variant of TASPG, called TAISG, that uses an informed search algorithm to reduce the number of visited nodes in the search procedure. The proposed method was compared with TASPG and other texture classification methods and showed good results, both in recognition rate and in computational cost.
%@language en
%3 PID4364901.pdf