Optimization-based multikernel extreme learning for multimodal object image classification

This paper is concerned with multi-kernel extreme learning machine (MK-ELM) which adapts the multi-kernel learning (MKL) framework to extreme learning machine (ELM). MK-ELM approach iteratively determines the combination of kernels by gradient descent wrapping a standard optimization method based ELM. Such MKL methods are very useful in information fusion research and applications. MK-ELM´s performance on object image classification via multimodal feature (visual and textual) fusion is experimented and studied. By comparing to other widely used fusion methods (i.e. SVM-based SimpleMKL, feature concatenation, and decision fusion), several advantages and characteristics of MK-ELM fusion are revealed and discussed showing MK-ELM is an easy and effective approach to implement in object image classification applications.