Generic Feature Learning in Computer Vision

• Published in: Procedia computer science 2015, Vol.58, p.202-209
• Main Authors: Nithin, D. Kanishka, Sivakumar, P. Bagavathi
• Format: Article
• Language: English
• Subjects: Autoencoders; Convolution Neural networks; Deep learning; Feature learning; Generic features; RBM; Recognition; Supervised learning; Unsupervised learning; Vision
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2015.08.054

Current Machine learning algorithms are highly dependent on manually designing features and the Performance of such algo- rithms predominantly depend on how good our representations are. Manually we might never be able to produce best and diverse set of features that closely describe all the variations that occur in our data. Understanding this, vision community is moving towards learning the optimum features itself instead of learning from the features. Traditional hand engineered features lack in generalizing well to other domains/Problems, are time consuming, expensive, requires expert knowledge on the problem domain and doesn’t facilitate learning from previous learnings/Representations(Transfer learning). All these issues are resolved in learning deep representations. Since 2006 a wide range of representation learning algorithms has been proposed but by the recent success and breakthroughs of few deep learning models, the representation learning algorithms have gained the spotlight. This paper aims to give short overview of deep learning approaches available for vision tasks. We also discuss their applicability (With respect to their properties) in vision field.