Wavelet subband-based steam detection by multiple kernel learning
Wavelet transform coefficients have been shown as significant features for detecting steam and smoke. Wavelet transform is multi-resolution in nature; moreover, at each resolution, wavelet transform coefficients form a high dimensional feature set. In this paper we handle both these issues in a multiple kernel learning (MKL) framework. First, high dimensionality is handled by using a kernel function that measures similarity between two sets of wavelet coefficients at the same resolution. Next, we consider a convex combination of these kernel functions that correspond to all the available resolutions of the wavelet transform. The proposed MKL uses an L1 norm linear support vector machine (SVM) for sparse learning of the convex combination. Then, this mixture kernel function is used in an L2 norm nonlinear SVM for binary classification- image with steam or without steam. Our method yields encouraging results and outperforms other competing methods.