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Common similarity measures of time domain signals such as cross-correlation and Symbolic Aggregate approximation (SAX) are not appropriate for nonlinear signal analysis. This is because of the high sensitivity of nonlinear systems to initial points. Therefore, a similarity measure for nonlinear signal analysis must be invariant to initial points and quantify the similarity by considering the main dynamics of signals. The statistical behavior of local extrema (SBLE) method was previously proposed to address this problem. The SBLE similarity index uses quantized amplitudes of local extrema to quantify the dynamical similarity of signals by considering patterns of sequential local extrema. By... 

Medical image stabilization has been widely used for clinical imaging modalities. Registration is a conspicuous stage for stabilizing dynamic medical images. Some of regular methods are sensitive to bias field distortion. Sparse-induced similarity measure (SISM) is a robust registering method against spatially-varying intensity distortions which is not evitable on clinical imaging instruments. This paper presents a method for registering medical images to average of captured images using SISM method to avoid spatially-varying intensity distortions like Bias field. Proposed method is compared with SSD and MI similarity measure based registrations. Results show enhancement in stabilizing... 

Similarity measures are very important concepts in fuzzy sets theory. There are several definitions for similarity measures in both type-1 and interval type-2 fuzzy sets (IT2-FSs). Here, the problem of finding similarity between two IT2-FSs is considered. A new similarity measure is proposed and its performance is compared with existing approaches in literature. Furthermore, an application of this measure in multiple criteria group decision making problem is given. Based on the proposed similarity measure, the satisfaction degree for different alternatives are established which then are used to rank alternatives in multiple criteria group decision making. Finally, a case study concerning the... 

Taxonomy learning is one of the major steps in ontology learning process. Manual construction of taxonomies is a time-consuming and cumbersome task. Recently many researchers have focused on automatic taxonomy learning, but still quality of generated taxonomies is not satisfactory. In this paper we have proposed a new compound similarity measure. This measure is based on both knowledge poor and knowledge rich approaches to find word similarity. We also used Neural Network model for combination of several similarity methods. We have compared our method with simple syntactic similarity measure. Our measure considerably improves the precision and recall of automatic generated taxonomies. ©... 

The shortest path problem is a classical and important network optimization problem appearing in many applications. We discuss the shortest path problem from a specified vertex to every other vertex on a network with imprecise arc lengths as fuzzy numbers. Using an order relation between fuzzy numbers, we propose a new algorithm to deal with the fuzzy shortest path problem. The algorithm is composed of a fuzzy shortest path length procedure and a similarity measure. The fuzzy shortest length method is proposed to find the fuzzy shortest length, and the fuzzy similarity measure is utilized to get the shortest path. Two illustrative examples are worked out to demonstrate the proposed algorithm... 

Defining a suitable similarity measure is a crucial step in (medical) image registration tasks. A common problem with frequently used intensity-based image registration algorithms is that they assume intensities of different pixels are independent of each other that could lead to low registration performance especially in the presence of spatially-varying intensity distortions, because they ignore the complex interactions between the pixel intensities. Motivated by this problem, in this paper we present a novel similarity measure which takes into account nonstationarity of the pixels intensity and complex spatially varying intensity distortions in mono-modal settings. Experimental results on... 

Bag of words models for feature extraction have demonstrated top-notch performance in image classification. These representations are usually accompanied by a coding method. Recently, methods that code a descriptor giving regard to its nearby bases have proved efficacious. These methods take into account the nonlinear structure of descriptors, since local similarities are a good approximation of global similarities. However, they confine their usage of the global similarities to nearby bases. In this paper, we propose a coding scheme that brings into focus the manifold structure of descriptors, and devise a method to compute the global similarities of descriptors to the bases. Given a local... 

The registration of images is a fundamental task in numerous applications in medical image processing. Similarity measure is an important key in intensity based image registration. Here, we propose correntropy measure as similarity measure in mono modal setting. Correntropy is a important measure between two random variables based on information theoretic learning and kernel methods. This measure is useful in non-Gaussian signal processing. In this paper, this measure is used in image registration. Here, we analytically illustrate that this measure is robust in presence of spiky noise (impulsive noise). The experimental results show that the proposed similarity has better performance than... 

The great surge in the research of community discovery in complex network is going on due to its challenging aspects. Dynamicity and overlapping nature are among the common characteristics of these networks which are the main focus of this paper. In this research, we attempt to approximate the granular human-inspired viewpoints of the networks. This is especially helpful when making decisions with partial knowledge. In line with the principle of granular computing, in which precision is avoided, we define the micro- and macrogranules in two levels of nodes and communities, respectively. The proposed algorithm takes microgranules as input and outputs meaningful communities in rough... 

Similarity measure is an important key in image registration. Most traditional intensity-based similarity measures (e.g. sum-of-squared-differences, correlation coefficient, mutual information and correlation ratio) assume a stationary image and pixel-by-pixel independence. These similarity measures ignore the correlation among pixel intensities; hence, a perfect image registration cannot be achieved especially in the presence of spatially varying intensity distortions and outlier objects that appear in one image but not in the other. It is supposed here that non-stationary intensity distortion (such as bias field) has a sparse representation in the transformation domain. Based on this... 

Elimination of motion artifact in breast MR images is a significant issue in pre-processing step before utilizing images for diagnostic applications. Breast MR Images are affected by slow varying intensity distortions as a result of contrast agent enhancement. Thus a nonrigid registration algorithm considering this effect is needed. Traditional similarity measures such as sum of squared differences and cross correlation, ignore the mentioned distortion. Therefore, efficient registration is not obtained. Residual complexity is a similarity measure that considers spatially varying intensity distortions by maximizing sparseness of the residual image. In this research, the results obtained by... 

Parallel corpora are the necessary resources in many multilingual natural language processing applications, including machine translation and cross-lingual information retrieval. Manual preparation of a large scale parallel corpus is a very time consuming and costly procedure. In this paper, the work towards building a sentence-level aligned English-Persian corpus in a semi-automated manner is presented. The design of the corpus, collection, and alignment process of the sentences is described. Two statistical similarity measures were used to find the similarities of sentence pairs. To verify the alignment process automatically, Google Translator was used. The corpus is based on news... 

Web sessions clustering is a process of web usage mining task that aims to group web sessions with similar trends and usage patterns into clusters. This process is crucial for effective website management, web personalization and developing web recommender systems. Accurate clustering of web sessions is highly dependent to the similarity measure defined to compare web sessions. In this paper, we propose a similarity measure for comparing web sessions. The sequential order of web navigations in sessions is considered using sequence alignment method. Furthermore, we propose to consider the usage similarity of two web sessions based on the time a user spends on a webpage, and also the frequency...