Abstract
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.
Highlights
Neural networks-based feature extracting for images has been widely applied in pattern recognitions
Some recently developed methods including Graph regularized NMF (GNMF), a Graph regularized Nonnegative Matrix Factorization method, LGNMF [36], an algorithm employed local centroid structured constraint to achieve sparse representation X, RSNMF [37], a semi-supervised nonnegative matrix factorization (NMF) which was introduced to obtain the robust discriminative representation, and MPMNMF [43], a multi-view clustering based NMF algorithm aimed to seek the manifold measurements in the decomposed factors
The images that we have selected for the tests include the following four datasets, and each image is resized to 32 × 32 gray scale for the neural network training and clustering
Summary
Neural networks-based feature extracting for images has been widely applied in pattern recognitions. On the other hand, when using manifold learning for classification or clustering, most current graph regularized NMF algorithms focused on learning the local invariance in a small neighborhood, which cannot completely obtain the intrinsic structures of objects to handle the complexity of images in different scenes. We propose a multiscale local manifold constrained NMF (LMNMF) algorithm, which can learn both locally linear representation and local invariance of images in different neighborhood scales to capture the low dimensional geometric architectures of sample data. The main contribution of this paper is summarized as follows: 1) A novel model called multi-scale local manifold regularized NMF is proposed In this model, NMF based manifold learning algorithms for both locally linear representation and local invariance of image data with different scales of neighborhoods are developed to extract the low dimensional geometric architectures of images in different scenes. Substituting (8) and (9) into (6), (7) respectively, and setting the step size parameters, the learning rules will be obtained
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