In this work, a novel approach for texture classification is proposed. We present a highly discriminative and simple descriptor to achieve feature learning and classification simultaneously for texture classification. The proposed method introduces the application of digital curvelet transform and explores feature reduction properties of locality sensitive discriminant analysis (LSDA) in conjunction with extreme learning machine (ELM) classifier. The image is mapped to the curvelet space. However, the curse of dimensionality problem arises when using the curvelet coefficients directly and therefore a reduction method is required. LSDA is used to reduce the data dimensionality to generate relevant features. These reduced features are used as the input to ELM classifier to analytically learn an optimal model. In contrast to traditional methods, the proposed method learns the features by the network itself and can be applied to more general applications. Extensive experiments conducted in two different domains using two benchmark databases, illustrate the effectiveness of the proposed method. In addition, empirical comparisons of the proposed method against curvelet transform in conjunction with traditional dimensionality reduction tools show that the suggested method does not only lead to a more reduced feature set, but it also outperforms all the compared methods in terms of accuracy.
Multimedia Tools and Applications – Springer Journals
Published: Dec 8, 2016
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera