Abstract
Texture analysis is an active area of research in computer vision and image processing, being one of the most studied topics for image characterization. When facing with texture analysis in a novel application a researcher needs to evaluate different texture methods and classifiers to verify which are the most suitable for each type of image. This usually leads the researcher to spend time setting code to make comparisons and tests. In this context, we propose a research and collaboration platform for the study, analysis, and comparison of texture descriptors and image datasets. This web-based application eases the creation of experiments in texture analysis that consists of extracting texture features and performing a classification over these features. It has a collection of methods, datasets, and classification algorithms while also allows the user to upload the code of new descriptors, the files of new texture datasets and to perform various tasks over them. Another interesting feature of this application is its interactive confusion matrix in which the researcher can identify correctly and incorrectly classified images.
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Acknowledgements
Alex J. F. Farfán acknowledges support from CNPq (Grant number #160871/2015-8) and CAPES (Grant number #PROEX-9527567/D). Leonardo F. S. Scabini acknowledges support from CNPq (Grant number #142438/2018-9). Odemir M. Bruno acknowledges support from CNPq (Grant #307797/2014-7 and Grant #484312/2013-8) and FAPESP (grant #14/08026-1 and #16/18809-9).
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Farfán, A.J.F., Scabini, L.F.S., Bruno, O.M. (2019). A Web-Based System to Assess Texture Analysis Methods and Datasets. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11679. Springer, Cham. https://doi.org/10.1007/978-3-030-29891-3_37
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