This paper introduces a new local feature description method to categorize scene images. We encode local image information by exploring the pseudo-Wigner distribution of images and the Local Binary Patterns (LBP) technique and make four major contributions. In particular, we first define a multi-neighborhood LBP for small image blocks. Second, we combine the multi-neighborhood LBP with the pseudo-Wigner distribution of images for feature extraction. Third, we derive the innovative WLBP feature vector by utilizing the frequency domain smoothing, the bag-of-words model and spatial pyramid representations of an image. Finally, we perform extensive experiments to evaluate the performance of the proposed WLBP descriptor. Specifically, we test our descriptor for classification performance using a Support Vector Machine (SVM) classifier on three fairly challenging publicly available image datasets, namely the UIUC Sports Event dataset, the Fifteen Scene Categories dataset and the MIT Scene dataset. Experimental results reveal that the proposed WLBP descriptor outperforms the traditional LBP technique and yields results better than some other popular image descriptors.