Search for: similarity-measure
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    A new similarity index for nonlinear signal analysis based on local extrema patterns

    , Article Physics Letters, Section A: General, Atomic and Solid State Physics ; Volume 382, Issue 5 , February , 2018 , Pages 288-299 ; 03759601 (ISSN) Niknazar, H ; Motie Nasrabadi, A ; Shamsollahi, M. B ; Sharif University of Technology
    Elsevier B.V  2018
    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 images stabilization using sparse-induced similarity measure

    , Article 2015 2nd International Conference on Pattern Recognition and Image Analysis, IPRIA 2015, 11 March 2015 through 12 March 2015 ; March , 2015 ; 9781479984459 (ISBN) Hariri, A ; Arabshahi, S ; Ghafari, A ; Fatemizadeh, E ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2015
    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... 

    Multiple attribute group decision making method using a new similarity measure in interval type-2 fuzzy sets: A case study

    , Article International Journal of Mathematics in Operational Research ; Volume 9, Issue 2 , 2016 , Pages 139-166 ; 17575850 (ISSN) Heidarzade, A ; Mahdavi, I ; Mahdavi Amiri, N ; Sharif University of Technology
    Inderscience Enterprises Ltd  2016
    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 construction using compound similarity measure

    , Article OTM Confederated International Conferences CoopIS, DOA, ODBASE, GADA, and IS 2007, Vilamoura, 25 November 2007 through 30 November 2007 ; Volume 4803 LNCS, Issue PART 1 , 2007 , Pages 915-932 ; 03029743 (ISSN); 9783540768463 (ISBN) Neshati, M ; Hassanabadi, L. S ; Sharif University of Technology
    Springer Verlag  2007
    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. ©... 

    Designing a new algorithm for the fuzzy shortest path problem in a network

    , Article 37th International Conference on Computers and Industrial Engineering 2007, Alexandria, 20 October 2007 through 23 October 2007 ; Volume 1 , 2007 , Pages 556-563 ; 9781627486811 (ISBN) Mahdavi, I ; Tajdin, A ; Nourifar, R ; Hasanzade, R ; Mahdavi Amiri, N ; Sharif University of Technology
    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... 

    Image Classication for Content Based Image Retrieval

    , M.Sc. Thesis Sharif University of Technology Saboorian, Mohammad Mehdi (Author) ; Jamzad, Mansour (Supervisor)
    In this project we tried to to solve the problem of clustring images of a large image database. Considering that there is no prior information regarding domain of the images, we will review unsupervised clustring methods. For this, we will discuss about image description vector and similarity measures. At last, our contribution will be about finding the optimum number of clusters with the least of user invervention. Results of runnig our method on a databse with 1000 images is reported and compared to a similar method named CLUE. Our result shows considerable improvements when user feedback taken to account.

    A new similarity measure for intensity-based image registration

    , Article Proceedings of the 4th International Conference on Computer and Knowledge Engineering, ICCKE 2014 ; 18 December , 2014 , Pages 227-232 ; ISBN: 9781479954865 Shirpour, M ; Aghajani, K ; Manzuri Shalmani, M. T ; Sharif University of Technology
    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... 

    From local similarity to global coding: An application to image classification

    , Article Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, OR ; 2013 , Pages 2794-2801 ; 10636919 (ISSN) Shaban, A ; Rabiee, H. R ; Farajtabar, M ; Ghazvininejad, M ; Sharif University of Technology
    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... 

    Mono-modal image registration via correntropy measure

    , Article Iranian Conference on Machine Vision and Image Processing, MVIP ; Sept , 2013 , Pages 223-226 ; 21666776 (ISSN) ; 9781467361842 (ISBN) Ghaffari, A ; Fatemizadeh, E ; Sharif University of Technology
    IEEE Computer Society  2013
    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... 

    A novel granular approach for detecting dynamic online communities in social network

    , Article Soft Computing ; 2018 ; 14327643 (ISSN) Cheraghchi, H. S ; Zakerolhosseini, A ; Bagheri Shouraki, S ; Homayounvala, E ; Sharif University of Technology
    Springer Verlag  2018
    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... 

    Cellular Learning Automata and Its Applications in Pattern Recognition

    , M.Sc. Thesis Sharif University of Technology Ahangaran, Meysam (Author) ; Beigy, Hamid (Supervisor)
    Cellular learning automata (CLA) is a distributed computational model that is introduced recently. This model is combination of cellular automata (CA) and learning automata (LA) and is used in many applications such as image processing, channel assignment in cellular networks, VLSI placement, rumor diffusion and modeling of commerce networks, and obtained acceptable results in these applications. This model consists of computational units called cells and each cell has one or more learning automata. In each stage, each automaton chooses an action from its actions set and applies it to the environment. Each cell has some neighboring cells that constitute its local environment. The local rule... 

    Image Registration by Mapping Defined-Functions Around Regions of Interest

    , M.Sc. Thesis Sharif University of Technology Sarikhani, Hossein (Author) ; Manzuri, Mohammad Taghi (Supervisor)
    Image registration is the process of matching two or more images captured at different times, angles or even sensors. Image registration is finding a transformation between two images. Image registration has many applications in different fields of Image Processing and machine vision. In the field of remote sensing, image registration can be used for environmental monitoring, generating graphical maps and gathering data for geogeraphical information systems, also in the field of medical image processing it can be used to detect tumor growth. Generally whenever there is need to extract information from many images; image registration is considered as an important pre-processing step.... 

    MR Image Registration under Variant Illumination

    , M.Sc. Thesis Sharif University of Technology Shirpour, Mohsen (Author) ; Manzouri, Mohammad Taghi (Supervisor)
    Image registration is defined as matching two or more than two images which are taken from a same scene in different times, from various views and using different sensors. By matching, we mean finding the transformation function between the images. Image registration
    has many application in medical domains such as analyzing changes in body limbs, supervision of the tumors growth, and robotic surgery. The challenges of image registration are unavailability of the transformation function, existence of the outliers in the images, changes in the images intensity, changes in the image geometry, noise, etc. Image registration consists of four steps: (i) feature extraction, (ii) choosing a... 

    Web User Profile Clustering Using Artificial Immune System

    , M.Sc. Thesis Sharif University of Technology Azimpour Kivi, Mozhgan (Author) ; Azmi, Reza (Supervisor) ; Khansari, Mohammad (Supervisor)
    Nowadays, a need for intelligent system that can assist web users in acquiring their desired information has been highlighted. In particular a mechanism for web user profiling which involves knowing the users and providing them with their preferred information is recommended to any website. To address this issue, Web Usage Mining (WUM) techniques have attracted many attentions. WUM is a kind of web mining process that tries to extract interesting usage patterns from the data that are obtained from the interaction of users with the web. The most important drawback of conventional, data mining based, WUM system is their inability to continuously learn the evolving patterns of web usage. On the... 

    Image Matching Based on Manifold Learning Methods

    , M.Sc. Thesis Sharif University of Technology Azampour, Mohammad Farid (Author) ; Fatemizadeh, Emad (Supervisor)
    Medical imaging is of interest because of information that will provide for doctors and registration is inevitable when we need to compare two or more images, taken from a subject at different times or with different sensors or when comparing two or more subjects together. Registration methods can be categorized in two major groups; methods based on feature and methods based on intensity. Methods in first group have three steps in common: feature extraction, finding matches and transform estimation. In second group it’s important to define a similarity measure and find the transform that minimizes this measure.
    Manifold learning algorithms are mostly used as a dimensionality reduction... 

    Sparse-induced similarity measure: Mono-modal image registration via sparse-induced similarity measure

    , Article IET Image Processing ; Volume 8, Issue 12 , 1 December , 2014 , Pages 728-741 ; ISSN: 17519659 Ghaffari, A ; Fatemizadeh, E ; Sharif University of Technology
    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... 

    Nonrigid registration of breast MR images using residual complexity similarity measure

    , Article Iranian Conference on Machine Vision and Image Processing, MVIP, Zanjan ; Sept , 2013 , Pages 241-244 ; 21666776 (ISSN); 9781467361842 (ISBN) Nekoo, A. H ; Ghaffari, A ; Fatemizadeh, E ; Sharif University of Technology
    IEEE Computer Society  2013
    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... 

    PEN: Parallel English-Persian news corpus

    , Article Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011, 18 2011 through 21 July 2011 ; Volume 2 , July , 2011 , Pages 523-528 ; 9781601321855 (ISBN) Farajian, M. A ; ICAI 2011
    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... 

    A webpage similarity measure for web sessions clustering using sequence alignment

    , Article 2011 International Symposium on Artificial Intelligence and Signal Processing, AISP 2011, 15 June 2011 through 16 June 2011 ; June , 2011 , Pages 20-24 ; 9781424498345 (ISBN) Azimpour Kivi, M ; Azmi, R ; Sharif University of Technology
    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... 

    Overlapped ontology partitioning based on semantic similarity measures

    , Article 2010 5th International Symposium on Telecommunications, IST 2010, 4 December 2010 through 6 December 2010 ; 2010 , Pages 1013-1018 ; 9781424481835 (ISBN) Etminani, K ; Rezaeian Delui, A ; Naghibzadeh, M ; Sharif University of Technology
    Today, public awareness about the benefits of using ontologies in information processing and the semantic web has increased. Since ontologies are useful in various applications, many large ontologies have been developed so far. But various areas like publication, maintenance, validation, processing, and security policies need further research. One way to better tackle these areas is to partition large ontologies into sub partitions. In this paper, we present a new method to partition large ontologies. For the proposed method, three steps are required: (1) transforming an ontology to a weighted graph, (2) partitioning the graph with an algorithm which recognizes the most important concepts,...