Subsequence clustering is an important issue in time series data mining. Observing that most time series consist of various patterns with different unknown lengths, we propose an optimization framework to adaptively estimate the lengths and representations for different patterns. Our framework minimizes the inner subsequence cluster errors with respect to subsequence clusters and segmentation under time series cover constraint where the subsequence cluster lengths can be variable. To optimize our framework, we first generate abundant initial subsequence clusters with different lengths. Then, three cluster operations, i.e., cluster splitting, combination and removing, are used to iteratively refine the cluster lengths and representations by respectively splitting clusters consisting of different patterns, joining neighboring clusters belonging to the same pattern and removing clusters to the predefined cluster number. During each cluster refinement, we employ an efficient algorithm to alternatively optimize subsequence clusters and segmentation based on dynamic programming. Our method can automatically and efficiently extract the unknown variable-length subsequence clusters in the time series. Comparative results with the state-of-the-art are conducted on various synthetic and real time series, and quantitative and qualitative performances demonstrate the effectiveness of our method.

We propose to integrate spectral-spatial feature extraction and tensor discriminant analysis for hyperspectral image classification. First, we apply remarkable spectral-spatial feature extraction approaches in the hyperspectral cube to extract a feature tensor for each pixel. Then, based on class label information, local tensor discriminant analysis is used to remove redundant information for subsequent classification procedure. The approach not only extracts sufficient spectral-spatial features from original hyperspectral images but also gets better feature representation owing to tensor framework. Comparative results on two benchmarks demonstrate the effectiveness of our method.

Classification of hyper spectral images is an important issue in remote sensing image processing systems. Hyper spectral images have advantages in pixel-wise classification owing to the high spectral resolution. However, the pixel-wise classification result often introduces the salt-and-pepper appearance because of the complex noise produced by atmosphere and instrument. An effective way to overcome this phenomenon is to resort to the spatial information. This paper proposes a method to solve the above problem by using spatial similarity information. First, in order to avoid the effect of noisy pixels and mixed pixels, reliable seeds are selected in local windows according to the agreement between the central pixel and its spatial neighbors. Then, the information of the reliable seeds is propagated to their spatial neighbors by a graph Laplacian. Specifically, the graph Laplacian is designed to propagate information among spatial neighbors with close similarity relationship so that some small or long thin objects are identified. Through the seed selection and local reliable information propagation, the problem of noisy labels is solved elegantly. Experiments on three real hyper spectral data sets with different spatial resolution, spectral resolution and land covers demonstrate the effectiveness of our method.

This paper presents a new deblurring method to remove the out-of-focus blur from similar image pairs. The method is motivated by an observation that a blurred structure appearing in one image can often have its corresponding clear one in the similar clear images. Our method first extracts the patch pairs from input images by SIFT matching. Then the constraints on the patch pairs are used to estimate the blur kernel via the RANSAC algorithm. Finally, the non-blind deconvolution is adopted to restore the blurred image. The main advantage is that we can improve the deblurring results with the help of additional similar clear images in many practical applications. Our method is validated on synthetic and real images by comparing with state-of-the-art methods.

Images captured in foggy weather conditions often suffer from bad visibility. In this paper, we propose an efficient regularization method to remove hazes from a single input image. Our method benefits much from an exploration on the inherent boundary constraint on the transmission function. This constraint, combined with a weighted L_1-norm based contextual regularization, is modeled into an optimization problem to estimate the unknown scene transmission. A quite efficient algorithm based on variable splitting is also presented to solve the problem. The proposed method requires only a few general assumptions and can restore a high-quality haze-free image with faithful colors and fine image details. Experimental results on a variety of haze images demonstrate the effectiveness and efficiency of the proposed method.

In this paper, we propose a novel group sparsity based semi-supervised band selection method. There are three key features in our method. First, it fulfills the band selection task by employing group sparsity on the regression coefficients in a robust linear regression for classification model, so that the selected bands hold lower classification errors. Second, the spatial smoothness prior is incorporated to preserve the similarity of spatial neighbors in band selection. Third, the objective function is efficiently optimized via an alternative iteration algorithm. Comparative results on two hyper-spectral data sets validate the effectiveness of our method, showing higher classification accuracies.

In this paper, we present an Integrated Semi-Supervised Graph (IntSSG) approach to automatically segment face from color-depth video. In the first step, IntSSG performs skin color detection and online SIFT matching to initialize some face and non-face pixels. Then, the labels of these pixels are refined by conducting adaptive depth thresholding. Finally, based on a semi-supervised graph framework, IntSSG segments face by propagating the refined labels to other pixels. Experimental results show that IntSSG is able to accurately segment faces in difficult situations such as large pose changes and illumination variations.

In this paper, we propose a metric rectification method to restore an image from a single camera-captured document image. The core idea is to construct an isometric image mesh by exploiting the geometry of page surface and camera. Our method uses a general cylindrical surface (GCS) to model the curved page shape. Under a few proper assumptions, the printed horizontal text lines are shown to be line convergent symmetric. This property is then used to constrain the estimation of various model parameters under perspective projection. We also introduce a paraperspective projection to approximate the nonlinear perspective projection. A set of close-form formulas is thus derived for the estimate of GCS directrix and document aspect ratio. Our method provides a straightforward framework for image metric rectification. It is insensitive to camera positions, viewing angles, and the shapes of document pages. To evaluate the proposed method, we implemented comprehensive experiments on both synthetic and real-captured images. The results demonstrate the efficiency of our method. We also carried out a comparative experiment on the public CBDAR2007 data set. The experimental results show that our method outperforms the state-of-the-art methods in terms of OCR accuracy and rectification errors.