A fast recognition framework based on extreme learning machine using hybrid object information

This paper presents a new supervised learning scheme, which uses hybrid information i.e. global and local object information, for accurate identification and classification at considerably high speed both in training and testing phase. The first contribution of this paper is a unique image representation using bidirectional two-dimensional PCA and Ferns style approach to represent global and local information, respectively, of an object. Secondly, the application of extreme learning machine supports reliable recognition with minimum error and learning speed approximately thousands of times faster than traditional neural networks. The proposed method is capable of classifying various datasets in a fraction of second compared to other modern algorithms that require at least 2–3 s per image [14].