Exploiting Linear Manifold Features With Parts-Based Representation in Various Scenes

Image recognition in complex scenes is a big challenge in computer vision. Manifold learning has become one of the most popular tools in the application of data dimensionality reduction and image recognition due to its efficiency in retrieving the intrinsic geometric features of image data. In this paper, we propose a new manifold feature extracting model based on the nonnegative matrix factorization (NMF) for image clustering in various scenes. In this model, Pearson distance with multiple manifold regulation constraints are adopted as the objective function to derive NMF based learning algorithms for the feature capturing of high dimensional data. With a variable neighborhood size in the learning, the proposed model can learn the linear features and at the same time learn the local similarity of images in multi-scale neighborhoods of a graph space. For different settings of learning parameters $\lambda _{lx}$ and $\lambda _{sx}$ , tests show that the proposed algorithms can efficiently retrieve low dimensional structures of images. Test results on four different image datasets demonstrate that the algorithms can achieve the state of art performance on the clustering of images in different types of scene.