A discriminative reference-based method for scene image categorization is presented in this letter. Reference-based image classification approach combined with K-SVD is approved to be a simple, efficient, and effective method for scene image categorization. It learns a subspace as a means of randomly selecting a reference-set and uses it to represent images. A good reference-set should be both representative and discriminative. More specifically, the reference-set subspace should well span the data space while maintaining low redundancy. To automatically select reference images, we adapt affinity propagation algorithm based on data similarity to gather a reference-set that is both representative and discriminative. We apply the discriminative reference-based method to the task of scene categorization on some benchmark datasets. Extensive experiment results demonstrate that the proposed scene categorization method with selected reference set achieves better performance and higher efficiency compared to the state-of-the-art methods.

Local descriptors, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT) are widely used in various computer applications. They emphasize different aspects of image contents. In this letter, we propose to combine them in sparse coding for categorizing scene images. First, we regularly extract LBP and SIFT features from training images. Then, corresponding to each feature, a visual word codebook is constructed. The obtained LBP and SIFT codebooks are used to create a two-dimensional table, in which each entry corresponds to an LBP visual word and a SIFT visual word. Given an input image, LBP and SIFT features extracted from the same positions of this image are encoded together based on sparse coding. After that, spatial max pooling is adopted to determine the image representation. Obtained image representations are converted into one-dimensional features and classified by utilizing SVM classifiers. Finally, we conduct extensive experiments on datasets of Scene Categories 8 and MIT 67 Indoor Scene to evaluate the proposed method. Obtained results demonstrate that combining features in the proposed manner is effective for scene categorization.

This paper presents a novel scale-rotation invariant generative model (SRIGM) and a kernel sparse representation classification (KSRC) method for scene categorization. Recently the sparse representation classification (SRC) methods have been highly successful in a number of image processing tasks. Despite its popularity, the SRC framework lucks the abilities to handle multi-class data with high inter-class similarity or high intra-class variation. The kernel random coordinate descent (KRCD) algorithm is proposed for 1 minimization in the kernel space under the KSRC framework. It allows the proposed method to obtain satisfactory classification accuracy when inter-class similarity is high. The training samples are partitioned in multiple scales and rotated in different resolutions to create a generative model that is invariant to scale and rotation changes. This model enables the KSRC framework to overcome the high intra-class variation problem for scene categorization. The experimental results show the proposed method obtains more stable performances than other existing state-of-art scene categorization methods.

This paper presents an efficient yet powerful codebook model, named classified codebook model, to categorize natural scene category. The current codebook model typically resorts to large codebook to obtain higher performance for scene categorization, which severely limits the practical applicability of the model. Our model formulates the codebook model with the theory of vector quantization, and thus uses the famous technique of classified vector quantization for scene-category modeling. The significant feature in our model is that it is beneficial for scene categorization, especially at small codebook size, while saving much computation complexity for quantization. We evaluate the proposed model on a well-known challenging scene dataset: 15 Natural Scenes. The experiments have demonstrated that our model can decrease the computation time for codebook generation. What is more, our model can get better performance for scene categorization, and the gain of performance becomes more pronounced at small codebook size.