Correlation-based Similarity Measure Research Articles

Sub-pixel registration of multi-resolution imagery by correlation matching of the bathymetry-related features.

Multispectral imaging plays a significant role in coastal mapping and monitoring applications. For tasks involving the integration of multiple overlapped images, precise co-registration of the multisource satellite images is a crucial preliminary step. However, due to the limited terrestrial area and insufficient landscape features, the traditional methods become less efficient or even invalid in offshore island environments. This study addresses the problem by exploring the feasibility of using bathymetry information for geometric registration of satellite imagery. Instead of using the ground control points (GCPs) or extracting the tie points from the landscape features, the band ratio values are extracted from the multispectral images and are subsequently matched between different images through a correlation-based similarity measure. By searching the optimum correlation within the positioning uncertainty radius, the translation between two satellite images is estimated. Thus, the geometric inconsistency between the multispectral images of different sources and resolutions is effectively reduced. This result is obtained by using the ample bathymetry features without the aid of the GCPs and the in-situ bathymetry data. The experimental results using GeoEye-1, Sentinel-2, and Landsat-8 images at Ganquan Island show that for an island setting with a limited terrestrial area, the developed method achieves sub-pixel registration accuracy (less than 2 m) in planimetry. The effect of the nonlinearity and outliers are accounted for using the Spearman correlation measure. The improvement in image alignment enables the integration of multispectral images of different sources and resolutions for producing an accurate and consistent interpretation for coastal comparative and synergistic applications.

Restoration of the electrocardiogram during mechanical cardiopulmonary resuscitation

Objective: An artefact-free electrocardiogram (ECG) is essential during cardiac arrest to decide therapy such as defibrillation. Mechanical cardiopulmonary resuscitation (CPR) devices cause movement artefacts that alter the ECG. This study analyzes the effectiveness of mechanical CPR artefact suppression filters to restore clinically relevant ECG information. Approach: In total, 495 10 s ECGs were used, of which 165 were in ventricular fibrillation (VF), 165 in organized rhythms (OR) and 165 contained mechanical CPR artefacts recorded during asystole. CPR artefacts and rhythms were mixed at controlled signal-to-noise ratios (SNRs), ranging from –20 dB to 10 dB. Mechanical artefacts were removed using least mean squares (LMS), recursive least squares (RLS) and Kalman filters. Performance was evaluated by comparing the clean and the restored ECGs in terms of restored SNR, correlation-based similarity measures, and clinically relevant features: QRS detection performance for OR, and dominant frequency, mean amplitude and waveform irregularity for VF. For each filter, a shock/no-shock support vector machine algorithm based on multiresolution analysis of the restored ECG was designed, and evaluated in terms of sensitivity (Se) and specificity (Sp). Main results: The RLS filter produced the largest correlation coefficient (0.80), the largest average increase in SNR (9.5 dB), and the best QRS detection performance. The LMS filter best restored VF with errors of 10.3% in dominant frequency, 18.1% in amplitude and 11.8% in waveform irregularity. The Se/Sp of the diagnosis of the restored ECG were 95.1/94.5% using the RLS filter and 97.0/91.4% using the LMS filter. Significance: Suitable filter configurations to restore ECG waveforms during mechanical CPR have been determined, allowing reliable clinical decisions without interrupting mechanical CPR therapy.

Area-Based Dense Image Matching with Subpixel Accuracy for Remote Sensing Applications: Practical Analysis and Comparative Study

Dense image matching is a crucial step in many image processing tasks. Subpixel accuracy and fractional measurement are commonly pursued, considering the image resolution and application requirement, especially in the field of remote sensing. In this study, we conducted a practical analysis and comparative study on area-based dense image matching with subpixel accuracy for remote sensing applications, with a specific focus on the subpixel capability and robustness. Twelve representative matching algorithms with two types of correlation-based similarity measures and seven types of subpixel methods were selected. The existing matching algorithms were compared and evaluated in a simulated experiment using synthetic image pairs with varying amounts of aliasing and two real applications of attitude jitter detection and disparity estimation. The experimental results indicate that there are two types of systematic errors: displacement-dependent errors, depending on the fractional values of displacement, and displacement-independent errors represented as unexpected wave artifacts in this study. In addition, the strengths and limitations of different matching algorithms on the robustness to these two types of systematic errors were investigated and discussed.

AgreeRelTrust—A Simple Implicit Trust Inference Model for Memory-Based Collaborative Filtering Recommendation Systems

Recommendation systems alleviate the problem of information overload by helping users find information relevant to their preference. Memory-based recommender systems use correlation-based similarity to measure the common interest among users. The trust between users is often used to address the issues associated with correlation-based similarity measures. However, in most applications, the trust relationships between users are not available. A popular method to extract the implicit trust relationship between users employs prediction accuracy. This method has several problems such as high computational cost and data sparsity. In this paper, addressing the problems associated with prediction accuracy-based trust extraction methods, we proposed a novel trust-based method called AgreeRelTrust. Unlike accuracy-based methods, this method does not require the calculation of initial prediction and the trust relationship is more meaningful. The collective agreements between any two users and their relative activities are fused to obtain the trust relationship. To evaluate the usefulness of our method, we applied it to three public data sets and compared the prediction accuracy with well-known collaborative filtering methods. The experimental results show our method has large improvements over the other methods.

A person re-identification framework by inlier-set group modeling for video surveillance

Recognizing the same person across different camera views is crucial and yet difficult task in video surveillance. The difficulty lies in finding the matched image-pair against drastic variations in appearances and structures of the individuals. In this article, we present a three stage person re-identification framework to establish the correspondence among persons observed across non-overlapping camera views. In first stage, we propose to apply an algorithm for handling the illumination variations in image pairs. A pyramidal body partitioning scheme is then introduced to handle the viewpoint variations, in which it segments the pedestrian image into several logical parts. In second stage, we formulate an ensemble weighted hypergraph partitioning strategy that divides the gallery candidates into a set of groups with high intra-group and low inter-group commonality. A weighing scheme is suggested to find the contribution of each feature channel towards defining a group. Furthermore, we generate a set of inlier groups for each probe, where the probability of finding the desired match pair is high. In final stage, contributory weights are fused with the correlation-based similarity measure to find the corresponding match within the inlier group. Extensive experiments are carried out on three challenging datasets to evaluate the effectiveness of our proposed framework. The experimental results demonstrate that the proposed framework can achieve better performance compared with the existing methods.

A Neuromorphic Person Re-Identification Framework for Video Surveillance

This paper presents a neuromorphic person re-identification (NPReId) framework to establish the correspondence among individuals observed across two disjoint camera views. The proposed framework comprises three modules ( observation , cognition , and contemplation ), inspired by the form-and-color-and-depth (FACADE) theory model of object recognition system. In the observation module, a semantic partitioning scheme is introduced to segment a pedestrian into several logical parts, and an exhaustive set of experiments have been carried out to select the best possible complementary feature cues. In the cognition module, an unsupervised procedure is suggested to partition the gallery candidates into multiple consensus clusters with high intra-cluster and low inter-cluster similarity. A supervised classifier is then deployed to learn the relationship between each gallery candidate and its associated cluster, which is subsequently used to identify a set of inlier consensus clusters. This module also includes weighing of contribution of each feature channel toward defining a consensus cluster. Finally, in the contemplation module, the contributory weights are employed in a correlation-based similarity measure to find the corresponding match within the inlier set. The proposed framework is compared with several state-of-the-art methods on three challenging data sets: VIPeR, iLIDS-VID, and CUHK01. The experimental results, with respect to recognition rates, demonstrate that the proposed framework can obtain superior performance as compared with the counterparts. The proposed framework, along with its low-rank bound property, further establishes its suitability in practical scenarios through yielding high cluster hit rate with low database penetration.

An alternative approach to measure similarity between two deterministic transient signals

In many practical engineering applications, it is often required to measure the similarity of two signals to gain insight into the conditions of a system. For example, an application that monitors machinery can regularly measure the signal of the vibration and compare it to a healthy reference signal in order to monitor whether or not any fault symptom is developing. Also in modal analysis, a frequency response function (FRF) from a finite element model (FEM) is often compared with an FRF from experimental modal analysis. Many different similarity measures are applicable in such cases, and correlation-based similarity measures may be most frequently used among these such as in the case where the correlation coefficient in the time domain and the frequency response assurance criterion (FRAC) in the frequency domain are used. Although correlation-based similarity measures may be particularly useful for random signals because they are based on probability and statistics, we frequently deal with signals that are largely deterministic and transient. Thus, it may be useful to develop another similarity measure that takes the characteristics of the deterministic transient signal properly into account. In this paper, an alternative approach to measure the similarity between two deterministic transient signals is proposed. This newly proposed similarity measure is based on the fictitious system frequency response function, and it consists of the magnitude similarity and the shape similarity. Finally, a few examples are presented to demonstrate the use of the proposed similarity measure.

Similarity analysis of functional connectivity with functional near-infrared spectroscopy.

One of the remaining challenges in functional connectivity (FC) studies is investigation of the temporal variability of FC networks. Recent studies focusing on the dynamic FC mostly use functional magnetic resonance imaging as an imaging tool to investigate the temporal variability of FC. We attempted to quantify this variability via analyzing the functional near-infrared spectroscopy (fNIRS) signals, which were recorded from the prefrontal cortex (PFC) of 12 healthy subjects during a Stroop test. Mutual information was used as a metric to determine functional connectivity between PFC regions. Two-dimensional correlation based similarity measure was used as a method to analyze within-subject and intersubject consistency of FC maps and how they change in time. We found that within-subject consistency (0.61±0.09 ) is higher than intersubject consistency (0.28±0.13 ). Within-subject consistency was not found to be task-specific. Results also revealed that there is a gradual change in FC patterns during a Stroop session for congruent and neutral conditions, where there is no such trend in the presence of an interference effect. In conclusion, we have demonstrated the between-subject, within-subject, and temporal variability of FC and the feasibility of using fNIRS for studying dynamic FC.

Probabilistic correlation-based similarity measure on text records

Large scale unstructured text records are stored in text attributes in databases and information systems, such as scientific citation records or news highlights. Approximate string matching techniques for full text retrieval, e.g., edit distance and cosine similarity, can be adopted for unstructured text record similarity evaluation. However, these techniques do not show the best performance when applied directly, owing to the difference between unstructured text records and full text. In particular, the information are limited in text records of short length, and various information formats such as abbreviation and data missing greatly affect the record similarity evaluation.In this paper, we propose a novel probabilistic correlation-based similarity measure. Rather than simply conducting the matching of tokens between two records, our similarity evaluation enriches the information of records by considering correlations of tokens. The probabilistic correlation between tokens is defined as the probability of them appearing together in the same records. Then we compute weights of tokens and discover correlations of records based on the probabilistic correlations of tokens. The extensive experimental results demonstrate the effectiveness of our proposed approach.

Improved Bases Selection in Acoustic Model Interpolation for Fast On-Line Adaptation

This work presents a novel bases selection approach for acoustic model interpolation based fast on-line adaptation. The proposed approach employs a correlation based similarity measure in the supervector domain (derived by concatenating the Gaussian mean parameters of the adapted models) for the selection of bases. This approach is found to greatly reduce the computational complexity in comparison to the Viterbi-alignment based bases search. Moreover, the proposed approach employs joint representation along with orthogonalization for the dynamic selection of bases. Consequently, the selected bases result in a much balanced coverage of phonetic contexts in the synthesized adapted model. The proposed technique is found to result in improved performance for all three modes of adaptation viz. the utterance-specific, the incremental and the batch modes. For utterance-specific mode, it achieves a relative improvement of 10.2% over baseline with only 3 to 5 seconds of adaptation data.

Benchmarking of Multivariate Similarity Measures for High-Content Screening Fingerprints in Phenotypic Drug Discovery

High-content screening (HCS) is a powerful tool for drug discovery being capable of measuring cellular responses to chemical disturbance in a high-throughput manner. HCS provides an image-based readout of cellular phenotypes, including features such as shape, intensity, or texture in a highly multiplexed and quantitative manner. The corresponding feature vectors can be used to characterize phenotypes and are thus defined as HCS fingerprints. Systematic analyses of HCS fingerprints allow for objective computational comparisons of cellular responses. Such comparisons therefore facilitate the detection of different compounds with different phenotypic outcomes from high-throughput HCS campaigns. Feature selection methods and similarity measures, as a basis for phenotype identification and clustering, are critical for the quality of such computational analyses. We systematically evaluated 16 different similarity measures in combination with linear and nonlinear feature selection methods for their potential to capture biologically relevant image features. Nonlinear correlation-based similarity measures such as Kendall’s τ and Spearman’s ρ perform well in most evaluation scenarios, outperforming other frequently used metrics (such as the Euclidian distance). We also present four novel modifications of the connectivity map similarity that surpass the original version, in our experiments. This study provides a basis for generic phenotypic analysis in future HCS campaigns.

A correlation based stochastic partitional algorithm for accurate cluster analysis

Most partitional clustering algorithms such as K–means, K–nearest neighbour, evolutionary techniques use distance based similarity measures to group the patterns of a data set. However the distance based algorithms may converge to local optima when there are large variations in the attributes of the data set, leading to improper clustering. In this paper we propose a simple stochastic partitional clustering algorithm based on a Pearson correlation based similarity measure. Experiments on real–life data sets demonstrate that the proposed method provides superior performance compared to distance based K–means algorithm.

Entropy based Log Chromaticity Projection for Real-time Stereo Matching

Most of the existing stereo matching algorithms will assume a similar corresponding color values between stereo images. In the real scenario, these color values are effected by several radiometric factors such as illumination direction, illumination color, camera parameters, etc, which results in different color values between the corresponding points. Hence, applying the stereo algorithm directly on the raw color values is not appropriate for the real-time environment. This paper proposes an entropy minimization based log chromaticity projection for stereo image, thereby extracting the invariant image, which is independent of illumination and color. The developed invariant image is the perfect measure for finding the similarity between the corresponding points. Normalized cross correlation based similarity measure is applied on the generated invariant image and the obtained disparity outperforms some of the local and global stereo algorithms.

Correlation-Coefficient-Based Fast Template Matching Through Partial Elimination

Partial computation elimination techniques are often used for fast template matching. At a particular search location, computations are prematurely terminated as soon as it is found that this location cannot compete with an already known best match location. Due to the nonmonotonic growth pattern of the correlation-based similarity measures, partial computation elimination techniques have been traditionally considered inapplicable to speed up these measures. In this paper, we show that partial elimination techniques may be applied to a correlation coefficient by using a monotonic formulation, and we propose basic-mode and extended-mode partial correlation elimination algorithms for fast template matching. The basic-mode algorithm is more efficient on small template sizes, whereas the extended mode is faster on medium and larger templates. We also propose a strategy to decide which algorithm to use for a given data set. To achieve a high speedup, elimination algorithms require an initial guess of the peak correlation value. We propose two initialization schemes including a coarse-to-fine scheme for larger templates and a two-stage technique for small- and medium-sized templates. Our proposed algorithms are exact, i.e., having exhaustive equivalent accuracy, and are compared with the existing fast techniques using real image data sets on a wide variety of template sizes. While the actual speedups are data dependent, in most cases, our proposed algorithms have been found to be significantly faster than the other algorithms.

Application of maximin correlation analysis to classifying protein environments for function prediction

More and more protein structures are being discovered, but most of these still have little functional information. Based on the assumption that structural resemblance would lead to functional similarity, researchers computationally compare a new structure with functionally annotated structures, for high-throughput function prediction. The effectiveness of this approach depends critically upon the quality of comparison. In particular, robust classification often becomes difficult when a function class is an aggregate of multiple subclasses, as is the case with protein annotations. For such multiple-subclass classification problems, an optimal method termed the maximin correlation analysis (MCA) was proposed. However, MCA has never been applied to automated protein function prediction although MCA can minimize the misclassification risk in the correlation-based nearest neighbor classification, thus increasing classification accuracy. In this article, we apply MCA to classifying three-dimensional protein local environment data derived from a subset of the protein data bank (PDB). In our framework, the MCA-based classifier outperformed the compared alternatives by 7–19% and 6–27% in terms of average sensitivity and specificity, respectively. Given that correlation-based similarity measures have been widely used for mining protein data, we expect that MCA would be employed to enhance other types of automated function prediction methods.

Content-based access to oral and maxillofacial radiographs

Content-based access (CBA) to medical image archives, i.e. data retrieval by means of image-based numerical features computed automatically, has capabilities to improve diagnostics, research and education. In this study, the applicability of CBA methods in dentomaxillofacial radiology is evaluated. Recent research has discovered numerical features that were successfully applied for an automatic categorization of radiographs. In our experiments, oral and maxillofacial radiographs were obtained from the day-to-day routine of a university hospital and labelled by an experienced dental radiologist regarding the technique and direction of imaging, as well as the displayed anatomy and biosystem. In total, 2000 radiographs of 71 classes with at least 10 samples per class were analysed. A combination of co-occurrence-based texture features and correlation-based similarity measures was used in leaving-one-out experiments for automatic classification. The impact of automatic detection and separation of multi-field images and automatic separability of biosystems were analysed. Automatic categorization yielded error rates of 23.20%, 7.95% and 4.40% with respect to a correct match within the first, fifth and tenth best returns. These figures improved to 23.05%, 7.00%, 4.20%, and 20.05%, 5.65% and 3.25% if automatic decomposition was applied and the classifier was optimized to the dentomaxillofacial imagery, respectively. The dentulous and implant systems were difficult to distinguish. Experiments on non-dental radiographs (10,000 images of 57 classes) yielded 12.6%, 5.6% and 3.6%. Using the same numerical features as in medical radiology, oral and maxillofacial radiographs can be reliably indexed by global texture features for CBA and data mining.

Correlation-maximizing surrogate gene space for visual mining of gene expression patterns in developing barley endosperm tissue.

BackgroundMicro- and macroarray technologies help acquire thousands of gene expression patterns covering important biological processes during plant ontogeny. Particularly, faithful visualization methods are beneficial for revealing interesting gene expression patterns and functional relationships of coexpressed genes. Such screening helps to gain deeper insights into regulatory behavior and cellular responses, as will be discussed for expression data of developing barley endosperm tissue. For that purpose, high-throughput multidimensional scaling (HiT-MDS), a recent method for similarity-preserving data embedding, is substantially refined and used for (a) assessing the quality and reliability of centroid gene expression patterns, and for (b) derivation of functional relationships of coexpressed genes of endosperm tissue during barley grain development (0–26 days after flowering).ResultsTemporal expression profiles of 4824 genes at 14 time points are faithfully embedded into two-dimensional displays. Thereby, similar shapes of coexpressed genes get closely grouped by a correlation-based similarity measure. As a main result, by using power transformation of correlation terms, a characteristic cloud of points with bipolar sandglass shape is obtained that is inherently connected to expression patterns of pre-storage, intermediate and storage phase of endosperm development.ConclusionThe new HiT-MDS-2 method helps to create global views of expression patterns and to validate centroids obtained from clustering programs. Furthermore, functional gene annotation for developing endosperm barley tissue is successfully mapped to the visualization, making easy localization of major centroids of enriched functional categories possible.

Generalized relevance LVQ ( GRLVQ) with correlation measures for gene expression analysis

A correlation-based similarity measure is derived for generalized relevance learning vector quantization ( GRLVQ). The resulting GRLVQ-C classifier makes Pearson correlation available in a classification cost framework where data prototypes and global attribute weighting terms are adapted into directions of minimum cost function values. In contrast to the Euclidean metric, the Pearson correlation measure makes input vector processing invariant to shifting and scaling transforms, which is a valuable feature for dealing with functional data and with intensity observations like gene expression patterns. Two types of data measures are derived from Pearson correlation in order to make its benefits for data processing available in compact prototype classification models. Fast convergence and high accuracies are demonstrated for cDNA-array gene expression data. Furthermore, the automatic attribute weighting of GRLVQ-C is successfully used to rate the functional relevance of analyzed genes.