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.