Feature extraction is one of the most important steps in computer vision tasks such as object recognition, image retrieval and image classification. It describes an image by a set of descriptors where the best one gives a high quality description and a low computation. In this study, the authors propose a novel descriptor called histogram of local and global features using speeded up robust feature (SURF) descriptor (HLG(SURF)) based on a combination of local features obtained by computation of Bag of words of SURF and global features issued from a novel operator called upper and lower local binary pattern (ULLBP) that encodes the texture analysis associated with wavelet transform. To enhance the effectiveness of the descriptor, the authors used the colour information. To evaluate the proposed method, the authors carried out experiments in different applications such as image retrieval and image classification. The performance of the suggested descriptor was evaluated by calculating both precision and recall values using the challenging Corel and COIL-100 datasets for image retrieval. For image classification, the performance was measured by the classification rate using the challenging Corel and MIT scene datasets. The experimental results showed that the proposed descriptor outperforms the existing state of the art results.