High-order local ternary patterns with locality preserving projection for smoke detection and image classification

It is a challenging task to recognize smoke from visual scenes due to large variations in the color, texture, shapes of smoke. To improve detection accuracy, we propose a novel feature extraction method by encoding high order directional derivatives at each pixel. We first quantize the directional derivatives into ternary values to generate Local Ternary Patterns (LTP). For the sake of simplification, each LTP code is usually decomposed into an upper LBP code and a lower LBP code, but this leads to loss of information. Hence, we use joint histograms to preserve the co-occurrence of upper and lower LBP codes for each order LTP. Then we concatenate all joint histograms from different orders to propose High-order Local Ternary Patterns (HLTP). To improve computational efficiency, we apply Locality Preserving Projection (LPP) to reduce the dimension of HLTP. To further improve performance, we present a noise resistant mechanism to remove noisy derivatives, and then propose HLTP based on Magnitudes of noise removed derivatives and values of Center pixels (HLTPMC). Finally, the Support Vector Machine (SVM) is used for training and classification. Experiments on large scale smoke data sets show that our method can achieve detection rates above 94% with false alarm rates below 1.33%. Experiments on a multi-class Brodatz texture data set also achieved good performance with low dimensional features. So our method has powerful discriminative capabilities and compact feature representation for multi-class image classification.