عنوان انگلیسی مقاله:
Classification of scaled texture patterns with transfer learning
ترجمه فارسی عنوان مقاله:
طبقه بندی الگوهای بافت مقیاس پذیر با یادگیری انتقال
Sciencedirect - Elsevier - Expert Systems With Applications, 120 (2019) 448-460: doi:10:1016/j:eswa:2018:11:033
Asaad M. Anam, Muhammad A. Rushdi
Classification of texture patterns with large scale variations poses a great challenge for expert and intel- ligent systems. A pure learning approach addresses this issue by including texture patterns at all scales in the training dataset. This approach makes the construction of an expert system quite costly and unre- alistic given the large variations in real-world texture scales and patterns. We propose a transfer learning approach where the full range of texture scales is available only for a small subset of the texture classes. Such a subset is used to learn the scaling map through partial least-square regression or coupled dic- tionary learning. Experimental results on classifiers equipped with the learned maps show promising reduction in training data scale variability with improved classification accuracy compared to the data- intensive pure learning approach. The proposed approach can be followed to build image-based expert systems of reasonable accuracy and limited data requirements.
Keywords: Texture classification | Texture scaling | Transfer learning | Partial least-square regression | Coupled dictionary learning | Local binary patterns