This paper addresses the problem of sound event classification, focusing on feature extraction methods which are robust in noisy environments. In real world, sound events can be easily exposed in a noisy situation causing corruption of distinctive temporal and spectral characteristics. Therefore, extracting robust features to represent these characteristics is important in achieving good classification performance. In this paper, we employ a combination of local binary pattern (LBP) and histogram of oriented gradient (HOG) which are motivated from image processing technique to capture local characteristics of a spectrogram image in the noisy sound events. Furthermore, a bag-of-audio-words (BoAW) method is also applied to the combination of LBP and HOG to capture global characteristics of the spectrogram image. The proposed method is evaluated on a database consisting hundreds of audio clips for two groups of sound events which are aimed at audio surveillance applications. Test sounds are classified at various noise conditions by using a support vector machine and the proposed method shows over 20% relative improvements in average compared to other conventional feature based BoAW methods.