High-spatial-resolution aerial photographs can provide detailed distribution of sea ice features. However, very few studies have ever considered shadows on the photographs for sea ice detection. In this letter, sea ice shadows, retrieved from 163 selected aerial photographs acquired on July 26, 2010, in a marginal-ice-zone area near Barrow, Alaska, utilizing an object-based classification scheme, are used to estimate the sea ice ridge attributes through local solar illumination geometry. The photograph-averaged ridge frequency (354.6-8908.7 km-2), length (1.22-10.33 m), and height (0.15-1.29 m) are obtained from the 163 photographs using batch processing. This letter provides an important batch processing method for ridge detection and ridge attribute retrieval from the high-resolution imagery of sea ice.

This paper presents a median based classifier which predicts the conditional median of the class label given the feature vector. The class label is defined through a hidden variable whose median is further modeled as an additive model of the feature vector. We propose to estimate the conditional median of the hidden variable given the feature vector in the framework of generic functional gradient algorithm [5]. An equivalent form of the definition of median is introduced, whose smoothed version is employed as the objective function. To fit the model for the conditional median, the proposed objective function is maximized by gradient ascent in functional space, updating the fitted model a small step in the gradient direction in each iteration. The resulting algorithm, Median Boost, is a boosting like procedure which obtains the informative features and the classifier at the same time. On the task of labeling building blocks in natural images, the comparison results show that Median Boost performs better than or similar to several alternatives.

Nonnegative matrix factorization is used extensively for feature extraction and clustering analysis. Recently many sparsity/sparseness constraints, such as L1penalty, are introduced for sparse nonnegative matrix factorization. Inspired by sparsity measures from linear regression model, this paper proposes to integrate nonnegative matrix factorization with another sparsity constraint, the elastic net. The experimental results of clustering analysis on three gene expression datasets demonstrate the effectiveness of the proposed method.

Automatically selecting a subset of genes with strong discriminative power is a very important step in classification problems based on gene expression data. Lasso and Dantzig selector are known to have automatic variable selection ability in linear regression analysis. This paper employs Lasso and Dantzig selector to select most informative genes for representing the class label as a linear function of gene expression data. The selected genes are further used to fit linear classifiers for cancer classification. On 3 publicly available cancer datasets, the experimental results show that in general, Lasso is more capable than Dantzig selector in selecting informative genes for classification.

We describe a new method for learning the conditional probability distribution of a binary-valued variable from labelled training examples. Our proposed Compositional Noisy-Logical Learning (CNLL) approach learns a noisy-logical distribution in a compositional manner. CNLL is an alternative to the well-known AdaBoost algorithm which performs coordinate descent on an alternative error measure. We describe two CNLL algorithms and test their performance compared to AdaBoost on two types of problem: (i) noisy-logical data (such as noisy exclusive-or), and (ii) four standard datasets from the UCI repository. Our results show that we outperform AdaBoost while using significantly fewer weak classifiers, thereby giving a more transparent classifier suitable for knowledge extraction.

This paper presents a new local edge-based level set model that does not use initial contours. Unlike traditional edge-based active contours that use gradient to detect edges, our model derives the neighborhood distribution and edge information with two different localized region-based operators: a Gaussian mixture model-based intensity distribution estimator and the Hueckel operator. We incorporate the operator outcomes into the recently proposed local binary fitting (LBF) model as local distribution fitting (LDF) model, which enables a model without the initial contour selection, i.e., the level set function can be initialized with a random constant instead of a distance map. Thus our model overcomes the initialization sensitivity problem of most active contours. In addition, with region-based edge detection, the proposed LDF model provides more accurate and robust segmentation. Experiments on both synthetic and real images show the improved performance of our proposed model over the LBF model.

This paper describes a coarse-to-fine learning based image registration algorithm which has particular advantages in dealing with multi-modality images. Many existing image registration algorithms [18] use a few designed terms or mutual information to measure the similarity between image pairs. Instead, we push the learning aspect by selecting and fusing a large number of features for measuring the similarity. Moreover, the similarity measure is carried in a coarse-to-fine strategy: global similarity measure is first performed to roughly locate the component, we then learn/compute similarity on the local image patches to capture the fine level information. When estimating the transformation parameters, we also engage a coarse-to-fine strategy. Off-the-shelf interest point detectors such as SIFT [12] have degraded results on medical images. We further push the learning idea to extract the main structures/landmarks. Our algorithm is illustrated on three applications: (1) registration of mouse brain images of different modalities, (2) registering human brain image of MRI T1 and T2 images, (3) faces of different expressions. We show greatly improved results over the existing algorithms based on either mutual information or geometric structures.

Object boundary detection and segmentation is a central problem in computer vision. The importance of combining low-level, mid-level, and high-level cues has been realized in recent literature. However, it is unclear how to efficiently and effectively engage and fuse different levels of information. In this paper, we emphasize a learning based approach to explore different levels of information, both implicitly and explicitly. First, we learn low-level cues for object boundaries and interior regions using a probabilistic boosting tree (PBT). Second, we learn short and long range context information based on the results from the first stage. Both stages implicitly contain object-specific information such as texture and local geometry, and it is shown that this implicit knowledge is extremely powerful. Third, we use high-level shape information explicitly to further refine the object segmentation and to parse the object into components. The algorithm is trained and tested on a challenging dataset of horses [2], and the results obtained are very encouraging compared with other approaches. In detailed experiments we show significantly better performance (e.g. F-values of 0.75 compared to 0.66) than the best comparable reported performance on this dataset. Furthermore, the system only needs 1.5 minutes for a typical image. Although our system is illustrated on horse images, the approach can be directly applied to detecting/segmenting other types of objects.

It is important to detect and extract the major cortical sulci from brain images, but manually annotating these sulci is a time-consuming task and requires the labeler to follow complex protocols. This paper proposes a learning-based algorithm for automated extraction of the major cortical sulci from magnetic resonance imaging (MRI) volumes and cortical surfaces. Unlike alternative methods for detecting the major cortical sulci, which use a small number of predefined rules based on properties of the cortical surface such as the mean curvature, our approach learns a discriminative model using the probabilistic boosting tree algorithm (PBT). PBT is a supervised learning approach which selects and combines hundreds of features at different scales, such as curvatures, gradients and shape index. Our method can be applied to either MRI volumes or cortical surfaces. It first outputs a probability map which indicates how likely each voxel lies on a major sulcal curve. Next, it applies dynamic programming to extract the best curve based on the probability map and a shape prior. The algorithm has almost no parameters to tune for extracting different major sulci. It is very fast (it runs in under 1 min per sulcus including the time to compute the discriminative models) due to efficient implementation of the features (e.g., using the integral volume to rapidly compute the responses of 3-D Haar filters). Because the algorithm can be applied to MRI volumes directly, there is no need to perform preprocessing such as tissue segmentation or mapping to a canonical space. The learning aspect of our approach makes the system very flexible and general. For illustration, we use volumes of the right hemisphere with several major cortical sulci manually labeled. The algorithm is tested on two groups of data, including some brains from patients with Williams Syndrome, and the results are very encouraging.

Recently Saul and Lee proposed a mixture model for discriminative classification of non-negative data via non-negative matrix factorization for feature extraction. In order to improve the generalization, this paper considers a sparse version of the model. The basic idea is to minimize the sum of the weights of un-normalized mixture models for posterior distributions according to regularization method. Experiments on CBCL face database and USPS digit data set assess the validity of the proposed approach.

In this paper, the linear support vector machine (LPSVM) algorithm is used to construct an over complete set of weak classifiers, and AdaBoost algorithm are adopted to select part of them to form a strong classifier. During the course of feature extraction and selection, the new method can minimize the classification error directly, whereas most previous works cannot do this. An important difference between this method and other methods is that the sparse features are learnt from the training set instead of being arbitrarily defined. Experiments demonstrate that the new algorithm performs well.

To overcome the vast computation of the standard support vector machines (SVMs), Lee and Mangasarian (see First SIAM International Conference on Data Mining, 2001) proposed reduced support vector machines (RSVM). But they select 'support vectors' randomly from the training set, and this will affect the test result. In this paper, we select some representative vectors as support vectors via a simple unsupervised clustering algorithm, and then apply the RSVM method on these vectors. The proposed method can get higher recognition accuracy with fewer support vectors compared to the original RSVM, with the advantage of reducing the running time significantly.

A very simple algorithm is used to construct an over complete set of linear sparse feature based classifiers, and AdaBoost algorithm is adopted to select part of them to form a strong classifier. During the course of feature extraction and selection, the new method minimizes the classification error directly, whereas most previous works cannot do this. An important difference between this method and other methods is that the sparse features are learned from the training set, instead of being arbitrarily defined. Experiments demonstrate that the new algorithm performs quite well.