This paper explores a new Local Binary Patterns (LBP) based image descriptor that makes use of the bag-of-words model to significantly improve classification performance for scene images. Specifically, first, a novel multi-neighborhood LBP is introduced for small image patches. Second, this multi-neighborhood LBP is combined with frequency domain smoothing to extract features from an image. Third, the features extracted are used with spatial pyramid matching (SPM) and bag-of-words representation to propose an innovative Bag of Words LBP (BoWL) descriptor. Next, a comparative assessment is done of the proposed BoWL descriptor and the conventional LBP descriptor for scene image classification using a Support Vector Machine (SVM) classifier. Further, the classification performance of the new BoWL descriptor is compared with the performance achieved by other researchers in recent years using some popular methods. Experiments with three fairly challenging publicly available image datasets show that the proposed BoWL descriptor not only yields significantly higher classification performance than LBP, but also generates results better than or at par with some other popular image descriptors.