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Abstract In this paper, a fast unsupervised deep fusion framework for change detection of multitemporal synthetic aperture radar (SAR) images is presented. It mainly aim at generating a difference image (DI) in the feature learning procedure by stacked auto-encoders (SAEs). Stacked auto-encoders, as one kind of deep neural network, can learn feature maps that retain the structural information but suppress the noise in the SAR images, which will be beneficial for DI generation. Compared with shallow network, the proposed framework can extract more available features, and be favorable for getting better change results. Different with other common deep neural networks, our proposed method does not need labeled data to train the network. In addition, we find a subset of the entire samples that appropriately represent the whole dataset to speed up the training of the deep neural network without under-fitting. Moreover, we design a fusion network structure that can combine ratio operator based method to ensure that the representations of higher layers are better than that of the lower ones. To summarize, the main contribution of our work lies in using of deep fusion network for generation of DI in a fast and unsupervised way. Experiments on four real SAR images confirm that our network performs better than traditional ratio methods and convolutional neural network. [ABSTRACT FROM AUTHOR]

Inspired by the compressive sensing (CS) theory, a new classifier based on compressed dictionary and Least Squares Support Vector Machine (LS-SVM) is proposed to deal with large scale problems. The coefficients of support vectors can be recovered from a few measurements if LS-SVM is approximated to sparse structure. Using the known Cholesky decomposition, we approximate the given kernel matrix to represent the coefficients of support vectors sparsely by a low-rank matrix that we have used as a dictionary. The proposed measurement matrix being coupled with the dictionary forms a compressed dictionary that proves to satisfy the restricted isometry property (RIP). Our classifier has the quality of low storage and computational complexity, high degree of sparsity and information preservation. Experiments on benchmark data sets show that our classifier has positive performance. [ABSTRACT FROM AUTHOR]

It remains one of the most challenging tasks to distinguish different terrain materials from a single SAR image. With the increase of ground resolution, it allows us to model the SAR image directly by exploiting spatial structures and texture information that are extracted by several machine learning approaches. In this paper, a novel feature learning approach is proposed to capture discriminant features of high-resolution SAR images. In the first stage, a weighted discriminant filter bank is learned from some labeled SAR image patches to generate low-level features. Then, the locality constraint is introduced to produce the high-level features in both the encoding and the spatial pooling procedure. In this work, the superpixels are employed as the basic operational units instead of the pixels for terrain classification. With some learned domain patterns which are learned from all of the high-level features of each pixel, the superpixel is characterized by a hyper-feature. In the last stage, a linear-kernel support vector machine is utilized to classify all of these hyper-features which are generated for each superpixel. The experimental results show a better classification performance of the proposed approach than several available state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Many works have been recently presented to extract efficient features for automatic target recognition of synthetic aperture radar (SAR) images. However, they are limited in the discriminative ability of similar targets and robustness to the remarkable speckle noises and background clutters existed in images. In this paper, we propose a Complementary Spatial Pyramid Coding (CSPC) approach in the framework of Spatial Pyramid Matching (SPM). Both the coding coefficients and coding residuals are explored to develop more discriminative and robust features for representing SAR images. Multiple codebooks are first built from some training example images, where each codebook is formulated by local features of a certain class of samples. Then multiple sparse coding models are developed to derive features of a target under these codebooks. Additionally, these coding residuals are further sparsely encoded in the same way to that of local features. Finally, the encoded local features and the residual features are pooled according to spatial pyramid respectively, then concatenated to form the complementary features for the subsequent classification. The experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) public database verify the superior performance of the proposed method to some related approaches. [ABSTRACT FROM AUTHOR]

Non-negative matrix factorization (NMF) can map high-dimensional data into a low-dimensional data space. Feature selection can eliminate the redundant and irrelevant features from the alternative features. In this paper, we propose a feature selection based dual-graph sparse non-negative matrix factorization (DSNMF) which can find an appropriate low dimensional representation of data by NMF and then select more discriminative features to further reduce the dimension of the low dimensional space by feature selection rather than reduce the dimension by only NMF or feature selection in many previous methods. DSNMF combines dual-graph model with non-negative matrix factorization, which can not only simultaneously preserve the geometric structures in both the data space and the feature space, but also make the two non-negative matrix factors update iteratively and interactively. In addition, DSNMF exerts L 2,1 - norm constraint on the non-negative matrix factor of the feature space to make full use of the sparse self-representation information. What's more, we propose a new local discriminative feature selection clustering called feature selection based dual-graph sparse non-negative matrix factorization for local discriminative clustering (DSNMF-LDC) whose clustering effects are better. We give the objective function, the iterative updating rules and the convergence proof. Our empirical study shows that DSNMF-LDC is robust and excellent in comparison to 9 feature selection algorithms and 7 clustering algorithms in clustering accuracy (ACC) and normalized mutual information (NMI). [ABSTRACT FROM AUTHOR]

Subspace representation based salient object detection has received increasing interests in recent years. However, due to the independent coding process of sparse reconstruction, the locality and the similarity among regions to be encoded are not explored. To preserve the locality and similarity of regions, a graph Laplacian regularization term is constructed as a smooth operator to alleviate the instability of the salient score in visual object. Then a new saliency map is calculated by incorporating this local graph regularizer into sparse reconstruction, which explicitly explores the local spatial structure of salient objects and thus obtains more uniform salient map. Moreover, we advance a heuristic object based dictionary from background superpixels, by which objects can be more accurately located. Experimental results on four large benchmark databases demonstrate that the proposed method performs favorably against fifteen recent state-of-the-art methods in terms of five evaluation criterions. [ABSTRACT FROM AUTHOR]

The advantage of using multifeature information for classification has been widely recognized. Representation-based methods with multifeature combination learning have only recently attracted increasing attention for hyperspectral classification. However, nonlinearity in data and the computational load of processing multifeature information and contextual information have been two thorny issues. In this paper, we present a fast joint sparse representation model with multifeature combination learning and its kernel extensions for hyperspectral imagery classification. For several complementary features (spectral, shape, and texture), the proposed model simultaneously acquires a representation vector for each type of feature and encourages the representation vectors to share a common sparsity pattern by imposing the joint sparsity ℓ r o w , 0 -norm regularization. Thus, the cross-feature information can be taken into account. For different features, different weights are assigned since they may not contribute equally to the final decision. Furthermore, kernel joint sparse representation model is presented to handle nonlinearity in the data. Kernel model projects the data into a high-dimensional space to improve the separability, achieving a better performance than the linear version. At the same time, we incorporate contextual neighborhood knowledge into the learned models. Experiments on several real hyperspectral images indicate that the proposed algorithms with much less memory requirements perform significantly faster than state-of-the-art algorithms, while exhibit highly competitive classification accuracy. [ABSTRACT FROM AUTHOR]

Feature selection algorithms eliminate irrelevant and redundant features, even the noise, while preserving the most representative features. They can reduce the dimension of the dataset, extract essential features in high dimensional data and improve learning quality. Existing feature selection algorithms are all carried out in data space. However, the information of feature space cannot be fully exploited. To compensate for this drawback, this paper proposes a novel feature selection algorithm for clustering, named self-representation based dual-graph regularized feature selection clustering (DFSC). It adopts the self-representation property that data can be represented by itself. Meanwhile, the local geometrical information of both data space and feature space are preserved simultaneously. By imposing the l 2,1 -norm constraint on the self-representation coefficients matrix in data space, DFSC can effectively select the most representative features for clustering. We give the objective function, develop iterative updating rules and provide the convergence proof. Two kinds of extensive experiments on some datasets demonstrate the effectiveness of DFSC. Extensive comparisons over several state-of-the-art feature selection algorithms illustrate that additionally considering the information of feature space based on self-representation property improves clustering quality. Meanwhile, because the additional feature selection process can select the most important features to preserve the intrinsic structure of dataset, the proposed algorithm achieves better clustering results compared with some co-clustering algorithms. [ABSTRACT FROM AUTHOR]

The current self-examples learning is a promising direction for image super-resolution (SR) task, but still has several main drawbacks: (i) less ability of adapting the neighboring and non-local information for self-regression function learning; (ii) less priors from the training data generated by recursively down-sampling the low resolution (LR) input. In this work, we propose an external learning assisted self-examples learning SR (Exter-SESR) framework to alleviate these issues, which is conducted in a two-stage procedure. The first part is a hybrid neural network (HNN) that takes large LR image patches as input and extracts compact features from external dataset. The learned features can offer better neighboring and non-local priors. Meanwhile, a part of HNN is able to estimate an initial high resolution (HR) image to address the second issue, since the new training data from the initial HR images does not only preserve the original prior, but also involve the extra ones from the external data. The second part is to further refine the SR output using a SESR based model. In addition, we analyze the effects of different self-examples models on the SR performance and find that Gaussian process regression (GPR) achieves superior performance. Experimental results on the benchmark show that our proposed method outperforms the existing SESR methods by a large margin in terms of both quantitative and qualitative measurements. [ABSTRACT FROM AUTHOR]

Matrix factorization (MF) is an increasingly important approach in the field of missing value prediction because recommender systems are rapidly becoming ubiquitous. MF-based collaborative filtering (CF) seeks to improve recommender performance by combining user–item matrix with MF. However, most MF-based approaches available at present could not obtain high prediction accuracy because of the sparse availability of user–item matrices in CF models. The present paper proposes a framework that involves two efficient MF, dynamic single-element-based CF-integrating manifold regularization (DSMMF) and dynamic single-element-based Tikhonov graph regularization non-negative MF (DSTNMF). The aim of this framework is to better use the intrinsic structure of user–item rating matrix and user/item content information, overcome the dimensionality curse and ill-posed problem of weighted graph NMF, and evade the frequent manipulations of indicator matrices that lack practicability. We validate the effectiveness of our proposed algorithms with respect to recommender performance by four indices on three datasets. We demonstrate that our proposed approaches lead to considerable improvement compared with several other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Sparse coding based Single Image Super-Resolution (SISR) technology has proved to be effective in generating High-Resolution (HR) image from a single Low-Resolution (LR) image. However, unsuitable codebook will bring unexpected structural details in the resultant HR images. In this paper, in order to find more reliable image features for formulating codebook, we propose new Curvelet Support Value Filters (CSVFs) for multiscale structural features extraction. By defining a local multi-scale and multi-directional Curvelet function to approximate images and casting a structural risk minimization constraint in this approximation, we can derive a set of Curvelet support values to reveal the most salient local scale-location-direction information of images. Then these features are used to construct more reliable codebook, and sparse coding based recovery is performed at multiple scale and directions via two coupled example datasets, to recover HR images. Some experiments are taken on realizing a 3X amplification of natural images, and the recovered results suggest its efficiency and superiority to its counterparts. [ABSTRACT FROM AUTHOR]

Many researchers have introduced tag information to recommender systems to improve the performance of traditional recommendation techniques. However, user-defined tags will usually suffer from many problems, such as sparsity, redundancy, and ambiguity. To address these problems, we propose a new recommendation algorithm based on deep neural networks. In the proposed algorithm, users׳ profiles are initially represented by tags and then a deep neural network model is used to extract the in-depth features from tag space layer by layer. In this way, representations of the raw data will become more abstract and advanced, and therefore the unique structure of tag space will be revealed automatically. Based on those extracted abstract features, users׳ profiles are updated and used for making recommendations. The experimental results demonstrate the usefulness of the proposed algorithm and show its superior performance over the clustering based recommendation algorithms. In addition, the impact of network depth on the algorithm performance is also investigated. [ABSTRACT FROM AUTHOR]