There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blocking, but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of “naturalness” in the image due to the presence of distortions, thereby leading to a holistic measure of quality. The underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model. No transformation to another coordinate frame (DCT, wavelet, etc.) is required, distinguishing it from prior NR IQA approaches. Despite its simplicity, we are able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms. BRISQUE has very low computational complexity, making it well suited for real time applications. BRISQUE features may be used for distortion-identification as well. To illustrate a new practical application of BRISQUE, we describe how a nonblind image denoising algorithm can be augmented with BRISQUE in order to perform blind image denoising. Results show that BRISQUE augmentation leads to performance improvements over state-of-the-art methods. A software release of BRISQUE is available online: http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip for public use and evaluation.

/pdf/no-reference-image-quality-assessment-in-the-spatial-domain-2mnlvf25ig.pdf

No-Reference Image Quality Assessment in the Spatial Domain

An important aim of research on the blind image quality assessment (IQA) problem is to devise perceptual models that can predict the quality of distorted images with as little prior knowledge of the images or their distortions as possible. Current state-of-the-art “general purpose” no reference (NR) IQA algorithms require knowledge about anticipated distortions in the form of training examples and corresponding human opinion scores. However we have recently derived a blind IQA model that only makes use of measurable deviations from statistical regularities observed in natural images, without training on human-rated distorted images, and, indeed without any exposure to distorted images. Thus, it is “completely blind.” The new IQA model, which we call the Natural Image Quality Evaluator (NIQE) is based on the construction of a “quality aware” collection of statistical features based on a simple and successful space domain natural scene statistic (NSS) model. These features are derived from a corpus of natural, undistorted images. Experimental results show that the new index delivers performance comparable to top performing NR IQA models that require training on large databases of human opinions of distorted images. A software release is available at http://live.ece.utexas.edu/research/quality/niqe_release.zip.

/pdf/making-a-completely-blind-image-quality-analyzer-4rsald8tn3.pdf

Making a “Completely Blind” Image Quality Analyzer

On robust estimation of the location parameter

Conventional feature extraction methods cannot fully exploit both the spectral and spatial information of hyperspectral imagery. In this paper, we propose an ensemble method of subspace independent component analysis (ICA) and edge-preserving filtering (EPF) for the classification of hyper-spectral data to achieve this task. First, several subsets are randomly selected from the original feature space. Second, ICA is used to extract spectral independent components followed by a recent and effective EPF method, rolling guidance filter (RGF), to produce spatial features. The spatial features are treated as the input of a random forest (RF) classifier. Finally, the classification results from each subset are integrated together to produce the final map. Experimental results on real hyperspectral data demonstrate the effectiveness of the proposed method. A sensitivity analysis of this new classifier is also performed.

/pdf/classification-of-hyperspectral-data-with-ensemble-of-19opfg6g9g.pdf

Classification of hyperspectral data with ensemble of subspace ICA and edge-preserving filtering

The invention provides a method for finding discontinuities in an image. This method makes it possible to detect discontinuities, i.e. faults, by being based on the heterogeneity of the distribution of the gradients of the points of the image being studied. The heterogeneity of the distribution of the gradients is characteristic of the discontinuities in a stratified medium. The method is based on the characteristics of the discontinuities within the set of geological markers in order to obtain a better determination of these discontinuities.

Method and program for finding discountinuities

This paper presents a new approach for unsupervised band selection in the context of hyperspectral imaging. The hyperspectral band selection (HBS) task is considered as a clustering problem: bands are clustered in the image space; one representative image is then kept for each cluster, to be part of the set of selected bands. The proposed clustering method falls into the family of information-maximization clustering, where mutual information between data features and cluster assignments is maximized. Inspired by a clustering method of this family, we adapt it to the HBS problem and extend it to the case of multiple image features. A pixel selection step is also integrated to reduce the spatial support of the feature vectors, thus mitigating the curse of dimensionality. Experiments with different standard data sets show that the bands selected with our algorithm lead to higher classification performance, in comparison with other state-of-the-art HBS methods.

/pdf/unsupervised-hyperspectral-band-selection-via-multi-feature-1xe4gshvpy.pdf

Unsupervised hyperspectral band selection via multi-feature information-maximization clustering

Rotation Forest (RoF) is a recent powerful decision tree (DT) ensemble classifier of hyperspectral images. RoF exploits random feature selection and data transformation techniques to improve both the diversity and accuracy of DT classifiers. Conventional RoF only considers data transformation on spectral information. To overcome this limitation, we propose a spectral and spatial RoF (SSRoF), to further improve the performance. In SSRoF, pixels are first smoothed by the multiscale (MS) spatial weight mean filtering. Then, spectral–spatial data transformation, which is based on a joint spectral and spatial rotation matrix, is introduced into the RoF. Finally, classification results obtained from each scale are integrated by a majority voting rule. Experimental results on two datasets indicate the competitive performance of the proposed method when compared to other state-of-the-art methods.

/pdf/spectral-spatial-rotation-forest-for-hyperspectral-image-s7omor3lu7.pdf

Spectral–Spatial Rotation Forest for Hyperspectral Image Classification

In this paper, we present a new parametric modeling of three-dimensional homogeneous volumes of textured images (3D texture) based on 3D Wold decomposition for discrete processes. This decomposition additively separates any 3D discrete homogeneous process into three mutually orthogonal components: a purely indeterministic, a "remote past" - type deterministic and an evanescent component. The evanescent component can be further decomposed into two orthogonal components called type 1 evanescent and type 2 evanescent. Assuming that the 3D image volume is a finite realization of a discrete homogeneous stochastic process, the 3D texture field can be uniquely represented as a sum of four mutually orthogonal components with different spectral properties. The aim of this paper is to present new parametric models able to describe both the spectral support and spatial characteristics of each component. A spectral decomposition algorithm to separate a 3D texture into structured and unstructured parts is also proposed.

Yannick Berthoumieu

Papers

Classification of hyperspectral data with ensemble of subspace ICA and edge-preserving filtering

Method and program for finding discountinuities

Unsupervised hyperspectral band selection via multi-feature information-maximization clustering

Spectral–Spatial Rotation Forest for Hyperspectral Image Classification

Three-Dimensional Textured Image Blocks Model Based on Wold Decomposition