Research on the Robust Image Representation Scheme for Natural Scene Categorization

Natural scene categorization is a challenging pattern classification problem and the image representation has deep impact on the classification performance. To improve the robustness and effectiveness of the image representation, a novel integrated scheme is proposed. Firstly, a feature combination method is adopted to generate a compound feature which contains the local texture and the spatial structure information for each image. Then an optimized dimensionality reduction algorithm is applied on the compound features to get lower dimensional and compressed feature representations. In the following, the dimensionality reduced features are clustered by a k-means based adaptive clustering algorithmto form a proper visual codebook, and each image is represented by the codebook histogram. Finally, the support vector machine is exploited to do the scene categorization tasks using the robust image representations. The proposed scheme is sufficiently evaluated on three well-known scene datasets. The experimental results show that our proposed method effectively enhances the image representation and outperforms the state-of-the-art approaches.