Abstract
Convolutional Neural Networks have proved extremely successful in object classification applications; however, their suitability for texture analysis largely remains to be established. We investigate the use of pre-trained CNNs as texture descriptors by tapping the output of the last fully connected layer, an approach that has proved its effectiveness in other domains. Comparison with classical descriptors based on signal processing or statistics over a range of standard databases suggests that CNNs may be more effective where the intra-class variability is large. Conversely, classical approaches may be preferable where classes are well defined and homogeneous.
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Acknowledgements
This work was partially supported by the Department of Engineering at the Università degli Studi di Perugia, Italy, under project BioMeTron – Fundamental research grant D.D. 20/2015 and by the Spanish Government under project AGL2014-56017-R.
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Bello-Cerezo, R., Bianconi, F., Cascianelli, S., Fravolini, M.L., di Maria, F., Smeraldi, F. (2018). Hand-Designed Local Image Descriptors vs. Off-the-Shelf CNN-Based Features for Texture Classification: An Experimental Comparison. In: De Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2017. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-319-59480-4_1
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