%0 Conference Proceedings
%T Assessing Texture Descriptors for Seismic Image Retrieval
%D 2017
%8 Oct. 17-20, 2017
%A Mattos, Andrea Britto,
%A Ferreira, Rodrigo S.,
%A Silva, Reinaldo M. da Gama e,
%A Riva, Mateus,
%A Brazil, Emilio Vital,
%@affiliation IBM Research
%@affiliation IBM Research
%@affiliation IBM Research
%@affiliation IME-USP
%@affiliation IBM Research
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ
%S Proceedings
%I IEEE Computer Society
%J Los Alamitos
%K image retrieval, seismic.
%X Much work has been done on the assessment of texture descriptors for image retrieval in many domains. In this work, we evaluate the accuracy and performance of three well-known texture descriptors -- Gabor Filters, GLCM, and LBP -- for seismic image retrieval. These subsurface images pose challenges yet not thoroughly investigated in previous works, which are addressed and evaluated in our experiments. We asked for domain experts to annotate two seismic cubes, Penobscot 3D and Netherlands F3, and used them to evaluate texture descriptors, corresponding parameters, and similarity metrics with the potential to retrieve visually similar regions of the considered datasets. While GLCM is used in the vast majority of works in this area, our findings indicate that LBP has the potential to produce satisfying results with lower computational cost.
%@language en
%3 Assessing Texture Descriptors for Seismic Image Retrieval.pdf