In this thesis, we propose an effective framework for sports scene classification by using a Hybrid Bags-of-Feature model. The Bag-of-Feature (BoF) model is a methodology which represents an image based on the histogram of visual codewords. With its successful performance, the BoF model has been widely exploited in computer vision such as scene classification and object recognition. Unlike traditional BoF models which resort to a single feature descriptor such as SIFT and thus exploit a single codebook, the proposed Hybrid BoF model employs two different types of codebooks; SIFT feature and modified LBP feature. The LBP descriptor is capable of capturing micro patterns of the image, hence it is suitable for texture classification. In contrast, Gradient based feature descriptors such as SIFT have been proven to be effective for object recognition. By taking advantage of properties of both SIFT and LBP, the proposed method improves the classification accuracy for sports scene. To this end, we design the hybrid type of BoF framework which encodes foreground objects (i.e., players) and background separately. More specifically, foreground objects (or regions containing distinct silhouettes) are encoded based on the SIFT descriptor and the remaining regions such as sky, lawn or floor of the gymnasium are encoded based on the LBP descriptor. To build a criterion for descriptor selection, we introduce a saliency pyramid based on the Phase Fourier Transform (PFT). The proposed method has been extensively tested, and experimental results show that the proposed framework is effective for sports scene classification compared to other various state-of-the-art methods.