Jinhuan Wen

Feature extraction of hyperspectral images based on preserving neighborhood discriminant embedding

A novel manifold learning feature extraction approach—preserving neighborhood discriminant embedding (PNDE) of hyperspectral image is proposed in this paper. The local geometrical and discriminant structure of the data manifold can be accurately characterized by within-class neighboring graph and between-class neighboring graph. Unlike manifold learning, such as LLE, Isomap and LE, which cannot deal with new test samples and images larger than 70×70, the method here can process full scene hyperspectral images. Experiments results on hyperspectral datasets and real-word datasets show that the proposed method can efficiently reduce the dimensionality while maintaining high classification accuracy. In addition, only a small amount of training samples are needed.

Robust image registration using adaptive coherent point drift method

Abstract. Coherent point drift (CPD) method is a powerful registration tool under the framework of the Gaussian mixture model (GMM). However, the global spatial structure of point sets is considered only without other forms of additional attribute information. The equivalent simplification of mixing parameters and the manual setting of the weight parameter in GMM make the CPD method less robust to outlier and have less flexibility. An adaptive CPD method is proposed to automatically determine the mixing parameters by embedding the local attribute information of features into the construction of GMM. In addition, the weight parameter is treated as an unknown parameter and automatically determined in the expectation-maximization algorithm. In image registration applications, the block-divided salient image disk extraction method is designed to detect sparse salient image features and local self-similarity is used as attribute information to describe the local neighborhood structure of each feature. The experimental results on optical images and remote sensing images show that the proposed method can significantly improve the matching performance.

Local discriminant non-negative matrix factorization feature extraction for hyperspectral image classification

Non-negative matrix factorization (NMF) ignores both the local geometric structure of and the discriminative information contained in a data set. A manifold geometry-based NMF dimension reduction method called local discriminant NMF (LDNMF) is proposed in this paper. LDNMF preserves not only the non-negativity but also the local geometric structure and discriminative information of the data. The local geometric and discriminant structure of the data manifold can be characterized by a within-class graph and a between-class graph. An efficient multiplicative updating procedure is produced, and its global convergence is guaranteed theoretically. Experimental results on two hyperspectral image data sets show that the proposed LDNMF is a powerful and promising tool for extracting hyperspectral image features.

Feature matching using modified projective nonnegative matrix factorization

We present a novel matching method to find the correspondences among different images containing the same object. In the proposed method, by considering each feature point-set as a matrix, two point-sets are projected onto a common subspace using modified projective nonnegative matrix factorization. The core idea of the proposed approach is to jointly factorize of the two feature matrices and the matching operate on embeddings of the two point-sets in the common subspace. Furthermore, it is robust to noise due to the merit of the subspace method. The proposed approach was tested for matching accuracy, and robustness to noise. Its performance on synthetic and real images was compared with state-of-the-art reference algorithms.

Description of Salient Features Combined with Local Self-Similarity for SAR Image Registration

Local feature descriptor plays an important role in image representation and is helpful to further image processing. This paper proposes a local feature descriptor based registration method for synthetic aperture radar (SAR) images. The proposed method starts with identifying evenly distributed features by applying the divided salient image disk (SID) extraction method. To describe the shape content of local neighborhood, local self-similarity (LSS) descriptor is built in the local normalized region with a suitable size for every detected feature. Finally, the correspondence is found by measuring the similarity between LSS descriptors. The registration experiments on SAR images demonstrate that the proposed method can be applied to SAR image registration.

Manifold-Preserving Common Subspace Factorization for Feature Matching

A method, called Manifold-preserving Common Subspace Factorization, is presented which can be used for feature matching. Motivated by the Graph Regularized Non-negative Matrix Factorization (GNMF) algorithm (Deng et al. 2011), we developed GNMF algorithm by considering a joint factorization of the two feature matrices, which share a common basis matrix. An iterative multiplicative updating algorithm is proposed to optimize the objective, and its convergence is guaranteed theoretically. Our feature matching algorithm operates on the new representations in the common subspace generated by basis vectors. Experiments are conducted on the synthetic and real-world data. The results show that the Manifold-preserving common subspace factorization algorithm provides better matching rates than other matrix-factorization techniques.

Supervised linear manifold learning feature extraction for hyperspectral image classification

A supervised neighborhood preserving embedding (SNPE) linear manifold learning feature extraction method for hyperspectral image classification is presented in this paper. A points k nearest neighbors is found by using new distance which is proposed according to prior class-label information. The new distance makes intra-class more tightly and inter-class more separately. SNPE overcomes the single manifold assumption of NPE. Data sets lay on (or near) multiple manifolds can be processed. Experimental results on AVIRIS hyperspectral data set demonstrate the effectiveness of our method.

Point pattern matching based on kernel partial least squares

Point pattern matching is an essential step in many image processing applications. This letter investigates the spectral approaches of point pattern matching, and presents a spectral feature matching algorithm based on kernel partial least squares (KPLS). Given the feature points of two images, we define position similarity matrices for the reference and sensed images, and extract the pattern vectors from the matrices using KPLS, which indicate the geometric distribution and the inner relationships of the feature points. Feature points matching are done using the bipartite graph matching method. Experiments conducted on both synthetic and real-world data demonstrate the robustness and invariance of the algorithm.

Point pattern matching based on manifold embedding

The problem of point pattern matching (PPM) is frequently encountered in computer vision, such as image registration and image matching. This paper investigates the manifold approaches to the problem of point pattern matching, and proposes a manifold correspondence based on Locally Linear Embedding (LLE). Our method operates on embeddings of the two data sets in the manifold space so as to get embedding features, which is invariance to rotation, scaling and translation (RST). By comparing the manifold embeddings of the points, we locate correspondences. We evaluate the method on both synthetic and real-world data, and experimental results demonstrate its high accuracy and robust to outliers.

Realization of Catmull-Clark Subdivision Algorithm Based on Guadrilateral Network

Subdivision technology is becoming one of important trends of innovation in CAD/CAM modeling systems. Based on 7researches on subdivision surface theory, in this paper, a valid algorithm of generation for subdivision surface is proposed. The data structures for implementing subdivision surfaces are analyzed. The algorithm of subdivision process is explained in detail, and the matrices of generating and the rules of connecting about this subdivision algorithm are presented. Meanwhile, the continuity of subdivision is also discussed. Finally, the examples demonstrate explicit and efficiency of our method. Keywords-Catmull-Clark subdivision; subdivision rules; guadrilateral network; regular hexahedron